Computerized medical self-diagnostic and treatment advice system

ABSTRACT

A system and method for providing computerized, knowledge-based medical diagnostic and treatment advice. The medical advice is provided to the general public over a telephone network. Two new authoring languages, interactive voice response and speech recognition are used to enable expert and general practitioner knowledge to be encoded for access by the public. “Meta” functions for time-density analysis of a number of factors regarding the number of medical complaints per unit of time are an integral part of the system. A semantic discrepancy evaluator routine along with a mental status examination are used to detect the consciousness level of a user of the system. A re-enter feature monitors the user&#39;s changing condition over time. A symptom severity analysis helps to respond to the changing conditions. System sensitivity factors may be changed at a global level or other levels to adjust the system advice as necessary.

RELATED APPLICATIONS

This application is a continuation of application Ser. No. 10/371,873,filed Feb. 20, 2003, which is a divisional of application Ser. No.09/265,226, filed Mar. 8, 1999, now U.S. Pat. No. 6,748,353, which is adivisional of application Ser. No. 08/866,881, filed May 30, 1997, nowU.S. Pat. No. 5,910,107, which is a divisional of application Ser. No.08/176,041, filed Dec. 29, 1993, now U.S. Pat. No. 5,660,176. The abovereferenced applications are hereby incorporated by reference in theirentirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to medical knowledge systems and, moreparticularly, to systems for giving medical advice to the general publicover a telephone network.

2. Description of the Related Technology

Health care costs currently represent 14% of the United States GrossNational Product and are rising faster than any other component of theConsumer Price Index. Moreover, usually because of an inability to payfor medical services, many people are deprived of access to even themost basic medical care and information.

Many people delay in obtaining, or are prevented from seeking, medicalattention because of cost, time constraints, or inconvenience. If thepublic had universal, unrestricted and easy access to medicalinformation, many diseases could be prevented. Likewise, the earlydetection and treatment of numerous diseases could keep many patientsfrom reaching the advanced stages of illness, the treatment of which isa significant part of the financial burden attributed to our nation'shealth care system. It is obvious that the United States is facinghealth-related issues of enormous proportions and that present solutionsare not robust.

One prior attempt at a solution to the health care problem is calledAsk-A-Nurse, wherein a group of nurses provide health information bytelephone around-the-clock. A person with a medical problem calls an 800number and describes the problem to the nurse. The nurse uses a computerfor general or diagnostic information on the ailment or complaintmentioned by the caller. The nurse may then refer the caller to a doctorfrom a computerized referral list for a contracting hospital or group ofhospitals. Client hospitals contract with Ask-A-Nurse to provide patientreferrals. A managed care option called Personal Health Advisor issimilar and adds the capability for the caller to hear prerecordedmessages on health topics 24 hours a day. Several problems exist withthese prior medical advice systems. First, these systems have high costsassociated with having a nurse answer each telephone call. Second, thecaller may have to belong to a participating health plan to utilize theservice. Third, if for some reason all nurses on a particular shifthappen to be busy and the caller has an emergency condition (that is notknown by the caller to be an emergency), precious time in gettingemergency services may be lost during the delay.

Another prior health system was developed by InterPractice Systems whichprovides a computerized service that answers health care questions andadvises people in their homes. A health maintenance organization (HMO)may provide this service to its members in a particular geographic area.To get advice at home, an HMO member connects a toaster-sized box to atelephone and calls a toll-free 800 number. Using a keyboard that ispart of the box, the user answers questions displayed on a screen of thebox relating to the user's symptoms. Depending on the answers, the usermight be told to try a home remedy, be called by a nurse or doctor, orbe given an appointment to be examined. A limitation of this system isthe additional expense of the electronics box, which could either bepurchased by the user for approximately $300 or purchased by the healthorganization with the expense to be passed on to the users. Anotherlimitation is that this service is directed to members of a particularcontracting health organization, such as an HMO. What is desired is asystem that does not require additional hardware for the basic service,but that utilizes the existing communication network. The desired systemshould be available for use by any person, not just members of a certainorganization.

A prior attempt at a health care solution for a limited set ofconditions is described in U.S. Pat. No. 4,712,562. A patient's bloodpressure and heart rate are measured and the measurements are sent viatelephone to a remote central computer for storage and analysis. Reportsare generated for submission to a physician or the patient. U.S. Pat.No. 4,531,527 describes a similar system, wherein the receiving officeunit automatically communicates with the physician under predeterminedemergency circumstances.

U.S. Pat. No. 4,838,275 discloses a device for a patient to lay on orsit in having electronics to measure multiple parameters related to apatient's health. These parameters are electronically transmitted to acentral surveillance and control office where a highly trained observerinteracts with the patient. The observer conducts routine diagnosticsessions except when an emergency is noted or from a patient-initiatedcommunication. The observer determines if a nonroutine therapeuticresponse is required, and if so facilitates such a response. Aspreviously mentioned, highly trained people are needed by this systemalong with the special measurement apparatus (embedded in a bed orchair).

Other prior attempts at a health care solution are typified by U.S. Pat.No. 5,012,411 which describes a portable self-contained apparatus formeasuring, storing and transmitting detected physiological informationto a remote location over a communication system. The information isevaluated by a physician or other health professional. As before, highlytrained people are necessary to utilize such an apparatus.

Several services to provide medical or pharmaceutical advice are nowavailable via “1-900” telephone numbers, e.g., “Doctors by Phone.” Theseservices are available 24 hours a day and 7 days a week. A group ofdoctors, including some specialties, is available to answer questionsabout health care or medical conditions for people anywhere in theUnited States who call the “1-900” telephone of one of the services. Agroup of registered pharmacists answers questions about medications forthe “1-900” pharmaceutical service.

SUMMARY OF THE INVENTION

The present solution to the health care problem is a computerizedmedical diagnostic and treatment advice (MDATA) system that is a medicalknowledge-based system designed to give medical advice to the generalpublic over the telephone network. The goal of the MDATA system is toprovide everyone with equal access to high quality, 100%-consistentmedical advice at a reasonable cost. The MDATA system provides callerswith extremely fast and virtually unlimited access to health careinformation, twenty-four hours a day, from any location around theworld. Health care advice is made available to an entire spectrum ofusers, from elderly patients confined to their homes to travelers in aforeign country with telephones in their cars.

The central ideas leading to the development of the MDATA system arebased on the following assumptions:

-   -   Nearly 90% of all patient complaints are confined to        approximately 100 medical problems.    -   Almost all primary care decisions involved in these 100 problems        can be made based upon information learned solely by obtaining a        detailed medical history. The results of the physical        examination, laboratory, and imaging studies only tend to        confirm a diagnosis.    -   The minimal amount of information that many doctors believe can        only be obtained from the physical examination can actually be        directly acquired from the patient when given appropriate        instructions.    -   In most cases, a face-to-face interaction between the doctor and        patient is not necessary. A detailed and well-constructed        history, along with physical findings elicited from the patient,        can be obtained over the telephone.    -   Medicine is basically diagnosis and treatment. Although        treatment recommendations change frequently, the fundamental        principles of making the diagnosis do not.    -   There is a significant delay between the time a new therapy is        recognized as safe and effective and the time physicians are        able to provide it to their patients.        These central ideas are utilized in the implementation of the        MDATA system.

A goal of the MDATA system is to give better medical advice than afamily practitioner who is unfamiliar with a patient, e.g., an on-callphysician. A person seeking medical advice frequently will not be ableto see or speak with his or her personal physician in a timely manner.The MDATA system provides medical advice whenever desired by thecaller—seven days a week/24 hours a day.

All previous medical algorithms, including those used in the military,are designed for face-to-face interactions. Self-help books generally donot consider age and sex in their algorithms. Furthermore, a book cannottake into account how many times a person has consulted the samealgorithm within a short period of time for the same problem. Themedical algorithms used by the MDATA system are designed for use in atelecommunications setting and overcome the deficiencies of self-helpbooks.

Previous medical advice systems do not do a time-density analysis for anumber of factors with regard to the number of complaints per unit oftime. The MDATA system uses “meta” functions to perform these analyses.

Previous medical advice algorithms do not have a way of detecting theconsciousness level of the person seeking consultation. The MDATA systeminvokes a “mental status examination” whenever a complaint or problemhas the possibility of an altered level of consciousness. In addition,the MDATA system uses “semantic discrepancy evaluator loops” which allowthe system to invoke the mental status exam if there are differences inanswers to the parallel threads of thought that are woven or embeddedinto the system.

Other medical advice systems do not have a “re-enter” feature to monitora patient's progress or worsening over time. The MDATA system checks forand responds to changing conditions over time.

Prior medical advice systems suffer from the inability to be nearlyinstantly up-dated as new medical information is made available. TheMDATA system regularly and frequently updates the treatment aspect ofthe system.

The computerized medical diagnostic and treatment advice (MDATA) systemis a medical knowledge-based system designed to give medical advice tothe general public over the telephone network. Using a new authoringlanguage, interactive voice response and speech recognition technology,the MDATA system encodes a highly useful core of expert and generalpractitioner diagnostic and treatment knowledge into a computerizedsystem for access by non-medically trained personnel.

The MDATA system does not provide advice for every medical problem, nordoes it make an exhaustive study of one vertical cross-section ofmedicine. Instead, the MDATA system provides up-to-date medical advicefor approximately one hundred of the most commonly encountered problemsin general practice and emergency medicine. It also provides valuableinformation to the public on any number of other medical topics.

As another embodiment of the MDATA system, a person desiring medicaladvice and having access to a personal computer (PC) loads a programinto the PC to produce a stand-alone medical diagnostic and treatmentadvice (SA-MDATA) system. Rather than listening to questions andresponding via touch tone keypresses or via voice, the user responds toquestions and directions displayed on the computer screen via a computerinput device, such as a keyboard or mouse. The diagnosis and/ortreatment recommendations provided by the MDATA system are the same asthat provided by the SA-MDATA system. The user of the SA-MDATA systemcan procure updates by contacting the MDATA system sponsor/administratorto obtain the most current treatment table information for a particulardiagnosis.

In one embodiment there is a computerized medical diagnostic systemcomprising a memory configured to store a data structure, wherein datain the data structure can be referenced by specification of a pluralityof attributes representative of a medical condition of a patient,wherein a first attribute corresponds to a cause of disease and a secondattribute corresponds to an anatomic system, and a processor, in datacommunication with the memory, configured to make a diagnosis based onthe data in the data structure.

In another embodiment there is a computerized method of medicaldiagnosis, the method comprising ascertaining a plurality of attributesof a medical condition of a patient, wherein a first attribute isassociated with a cause of disease and a second attribute is associatedwith an anatomic system, modifying a data structure based on theattributes, and diagnosing the medical condition of a patient based onthe data structure.

In yet another embodiment there is a computerized method of medicaldiagnosis, the method comprising associating a data structure, stored ina memory on a computer, with a patient, wherein data in the datastructure can be referenced by specification of a plurality ofattributes representative of a medical condition of the patient, whereina first attribute corresponds to a cause of disease and a secondattribute corresponds to an anatomic system, ascertaining a plurality ofattributes of a first medical condition of the patient, modifying thestored data structure based on the plurality of attributes of the firstmedical condition, ascertaining a plurality of attributes of a secondmedical condition of the patient, modifying the stored data structurebased on the plurality of attributes of the second medical condition,and outputting a diagnosis based on the data in the data structure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the components of a presentlypreferred embodiment of the computerized medical diagnostic andtreatment advice (MDATA) system of the present invention;

FIG. 2 is a diagram of the off-line process used in producing the speechfiles shown in FIG. 1;

FIG. 3 is a diagram of the Node Translation process used in creatingfiles for use by the system of FIG. 1;

FIG. 4 is a diagram of some of the files and components of FIGS. 1 and 3that are utilized at run time;

FIG. 5 a is a diagram of the utilization of the files shown in FIG. 3 atrun time;

FIGS. 5 b-5 g are an exemplary sequence of data structures of the systemshown in FIG. 1 at run time;

FIG. 6 is a block diagram illustrating a conceptual view of the databasefiles and processes of the system of FIG. 1;

FIGS. 7 a, 7 b, 7 c and 7 d are a top-level flow diagram of the MDATAsystem of FIG. 1;

FIGS. 8 a and 8 b are a flow diagram of the patient login process 250defined in FIG. 7 a;

FIGS. 9 a and 9 b are a flow diagram of the patient registration process252 defined in FIG. 7 a;

FIGS. 10 a and 10 b are a flow diagram of the evaluation process 254defined in FIG. 7 d;

FIGS. 11 a and 11 b are a flow diagram of the meta function 500 definedin FIG. 10 b;

FIGS. 12 a and 12 b are a flow diagram of the assistant login process272 defined in FIG. 7 b;

FIGS. 13 a and 13 b are a flow diagram of the assisted patient loginprocess 276 defined in FIG. 7 b;

FIGS. 14 a and 14 b are a flow diagram of the assistant registrationprocess 274 defined in FIG. 7 b;

FIGS. 15 a and 15 b are a flow diagram of the assisted patientregistration process 278 defined in FIG. 7 b;

FIGS. 16 a and 16 b are a flow diagram of the mental status examinationfunction 508 defined in FIG. 10 b;

FIG. 17 is a flow diagram of the semantic discrepancy evaluator routine(SDER) 510 defined in FIG. 10 b;

FIG. 18 is a flow diagram of the past medical history routine 512defined in FIG. 10 b;

FIG. 19 is a flow diagram of the physical self examination function 514defined in FIG. 10 b;

FIG. 20 is a flow diagram of the patient medical condition routine 516defined in FIG. 10 b;

FIG. 21 is a flow diagram of the symptom severity analysis function 518defined in FIG. 10 b;

FIG. 22 is a flow diagram of the treatment table process 256 defined inFIG. 7 d;

FIG. 23 is a flow diagram of the menu-driven treatment selection process864 defined in FIG. 22;

FIG. 24 is Table 3, a two dimensional array of causes of diseasesplotted against anatomical systems;

FIG. 25 is Table 4, an exemplary plot of symptom severity versus time;

FIG. 26 is Table 5, another exemplary plot of symptom severity versustime; and

FIG. 27 is Table 9, a plot of sensitivity and selectivity versus time.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The following detailed description of the preferred embodiments presentsa description of certain specific embodiments to assist in understandingthe claims. However, the present invention can be embodied in amultitude of different ways as defined and covered by the claims.

For convenience, the following description will be outlined into thefollowing 22 principal sections: Introduction, System Overview,Operating Features of the MDATA System, Authoring Language, Run-TimeOperation, Software Structure, Top-Level Flow, Login Process,Registration Process, Evaluation Process, The Meta Function, MentalStatus Examination, Semantic Discrepancy Evaluator Routine, Past MedicalHistory Routine, Physical Self Examination, Symptom Severity Analysis,Treatment Table, The MDATA System Paradigm, Video Imaging, Benefits ofthe MDATA System, Optional System Configuration, and Summary ofAdvantages of the Present Invention.

I. Introduction

A consultation for a person seeking medical advice begins with atelephone call to the medical diagnostic and treatment advice (MDATA)system of the present invention. The MDATA system asks the callerspecific questions and then analyzes each response.

Voice recognition and interactive voice response technology allowcallers to respond to yes/no and multiple choice questions either byspeaking directly into the telephone or by using the touch tone pad oftheir telephone.

Easy access to the information in the MDATA system is made possible by anatural user interface. The computer-driven dialogue consists of simpleyes/no and multiple choice questions. The questions and treatmentrecommendations are very simply worded yet skillfully designed toreflect the accumulated experience of many physicians in conductingpatient interviews.

Although all the MDATA system's questions are designed to be easilyunderstood, unforeseen situations will inevitably arise. For thisreason, hierarchical staffing is implemented. As an example, for every10 telephone lines, one operator fully trained in triage and the MDATAsystem will be available. For every 10 operators there will be oneregistered nurse in attendance; and for every 10 registered nurses,there will be one physician in attendance. Staffing requirements areadjusted as the system is refined toward optimal efficiency. The MDATAsystem does not require the operator or the registered nurse to make anymedical decisions.

II. System Overview

Referring to FIG. 1, the components of a presently preferred embodimentof the computerized medical diagnostic and treatment advice (MDATA)system 100 of the present invention are shown. A personal computer (PC)102 includes a plurality of components within an enclosure 104. Aplurality of telephone lines 106 interface the public telephone network108 to the computer 102. As an example, one of telephone lines 106 isshown to be switched via network 108 to connect with a telephone 110that is used by a person desiring medical advice (user) 112. Throughoutthis document, the words user, caller and patient are usedinterchangeably. However, it will be understood that the caller may beacting as a proxy for the patient. If this is the case, the caller willbe registered as an assistant for the patient.

The hardware and system software were assembled with two basic conceptsin mind: portability to other operating systems and the use of industrystandard components. In this way, the system can be more flexible andwill allow free market competition to continuously improve the product,while, at the same time, decrease costs. While specific hardware andsoftware will be referenced, it will be understood that a panoply ofdifferent components could be used in the present system.

The system currently runs on the PC 102 with an Intel 80486microprocessor. “Telephony” functions use Dialogic Corporation's D/41Dvoice processing board 122 based on a digital signal processor (DSP).The voice processing (VP) board 122 performs several functions includinginterfacing the telephone lines, decoding touch tone signals, speechrecording and speech playback. Touch tone signals are also known as“dual tone multiple frequency” (DTMF) signals. A group of one to fourtelephone lines 106 connect to the VP board 122. The computer 102 mayinclude a plurality of VP boards 122 based on how many phone lineconnections are desired for the system 100. Speech recognition isachieved using Voice Processing Corporation's speech recognition VPRO-4board 124 (also DSP based). The voice recognition (VR) board 124performs several functions including recognizing utterances andreturning an index number of a recognition confidence level. The VRboard 124 and the VP board 122 both connect to an industry standardarchitecture (ISA) bus 126. The ISA bus 126 interconnects themicroprocessor 120 with a plurality of peripherals through controllercircuits (chips or boards).

The VP board 122 also connects to a VPRO-Adapt board 128 via an analogaudio bus 130 that is called Analog Extension Bus. Four simultaneouschannels provide a 96 kbit/second data transfer rate. Each channelcorresponds to a telephone line connected to the VP board 122 and isassociated with a current patient consultation. The Adapt board 128further connects to a digital audio bus 132. The VR board 124 alsoconnects to the digital audio bus 132. The Adapt board 128 performsanalog to digital signal conversion to a VPC-proprietary digital pulsecode modulation (PCM) format. The digital bus 132 can accommodate 32channels and has a data transfer rate of 2.048 Mbits/second.

The computer ISA bus 126 has a plurality of peripherals connected to itthrough adapters or controllers. A video adapter board 136, preferablyat VGA or better resolution, interconnects to a video monitor 138. Aserial communication circuit 140 interfaces a pointing device, such as amouse 142. A parallel communication circuit may be used in place ofcircuit 140 in another embodiment. A keyboard controller circuit 144interfaces a keyboard 146. A small computer systems interface (SCSI)adapter, such as model 1542C made by Adaptec, provides a SCSI bus 150 towhich a 500 Mb or greater hard disk drive 152 and dual Bernoulli 150 Mbdisk drives are preferably attached. The hard drive 152 stores databasefiles such as the patient files, speech files, and binary support files.

A main memory 156 connects to the microprocessor 120. In the presentlypreferred embodiment, the MDATA system 100 operates under DOS version5.0 operating system 158. The system software is written in MicrosoftC\C++ version 7.0 using structured programming techniques. An algorithmprocessor 160 includes a parser and supporting functions that manipulatea memory variable symbol table and a run time stack, which will bedescribed hereinbelow. Sequiter Software Inc. Codebase 5.0 allows accessto X-base compatible database records stored on the hard drive 152. TheMDATA system 100 also includes two new authoring languages (one each isused in two embodiments of the system), which will be discussedhereinbelow.

The system software includes the following code modules:

-   -   A. main.c—a collection of functions that mostly deal with        telephony functions, such as answering the phone line, speech        file playback, and DTMF tone collection. Global data structures        are defined here.    -   B. base.c—functions that invoke the CodeBase revision 5.0        library to perform xbase file manipulation.    -   C. pars.c—the parse function, and supporting functions that        manipulate the memory variable symbol table and run time stack.    -   D. regi.c—an on-line patient registration module.    -   E. resp.c—gets the caller's responses, either DTMF or voice, and        figures out what to do next by obeying a command (e.g., “repeat”        or “backup”), or traversing through the algorithm node map.    -   F. term.c—a useful collection of text phrases for Dialogic and        VPC board termination events and error codes.    -   G. user.c—“non-diagnostic” portions of the caller session:        initial screening questions, caller login, and the next node        playback initiator.    -   H. util.c—a collection of general purpose functions shared by a        run time executable, a node editor and ASCII translator tools.    -   I. view.c—a module that controls the graphics system display.    -   J. x10.c—an X-10 computer interface routine for fault recovery.    -   K. xlat.c—a module linked with pars.c and util.c object modules        to build xlat.exe, a stand-alone translation executable for        offline ASCII text file translation.        The application is compiled with the Microsoft graphics,        Dialogic board, VPC board and CodeBase database libraries.

The Voice Processing Corporation (VPC) VPro-4 VR board has eight voicerecognition channels, which by default are associated one-to-one withthe Dialogic D/41D channels. VPC's pioneering work in the voiceprocessing field is in the area of continuous speech. This allows aperson to speak a multiple digit number in a natural manner, withoutpausing after each digit. VPC supplies two continuous speechvocabularies: one vocabulary contains the digits 1 through 9, plus“zero” and “oh”, and the other contains just the two words “yes” and“no”. The vendor-supplied digits continuous speech vocabulary is used bythe system 100. In the presently preferred embodiment, if the score is75% or better, the response is unconditionally accepted. If the score isbetween 20% and 74%, the digits recognized are read back, and the calleris asked to accept or reject the digits. In another embodiment of thesystem 100, the above score thresholds are implemented as tunableparameters. The scoring parameters are stored in a configuration filethat is manipulated off-line by a utility program and is read by therun-time system at initialization.

VPC also provides a few discrete vocabularies. Discrete vocabulariescontain one or two word utterances. The vendor-supplied discrete speechvocabulary of the months of the year is used in the on-line patientregistration process. A speaker-independent discrete speech vocabularyconsisting of the words “yes”, “no” “backup”, “continue”, “help”,“operator”, “pause”, “quit” and “repeat” has been developed using a verypowerful set of utilities supplied by VPC, Scripter and Trainer. Theseutilities are for collecting samples and training the vocabulary.

The VR board 124 has the minimum of two MB memory installed. The defaultmemory configuration has a partition for both continuous vocabulariesand a partition for one discrete vocabulary. Additional discretevocabularies may be downloaded if the on-board memory is reconfigured.

The VR board 124 has four digital signal processors (DSP's) from whichVPC derived eight voice recognition channels. Each of these eightrecognition resources is referred to as a VPro Speech Processor (VSP).Discrete vocabulary recognition requires one VSP; continuous vocabularyrecognition requires two adjacent VSP's. The MDATA system 100 has a VSPresource manager in the resp.c software module. This resource managerallocates VSP's in a dynamic manner to VP board 122 channels on a demandbasis. As soon as the system receives a response, voice or DTMF, itreleases the VSP's associated with the caller's VP board 122 channel.

The MDATA system 100 uses VPC's application programming interface (API)for the C programming language. This makes the application vendorspecific to VPC, but also allows the system 100 to utilize all thepowerful API features, e.g., on-line creation of discrete speakerdependent vocabularies used for voice pattern matching or voiceprinting.

The VPC API supports both continuous speech vocabulary (CSV) anddiscrete speech vocabulary (DSV) recognition.

The voice processing (VP) board 122 supports speech recording andplayback, as well as touch tone (DTMF) signal detection and decoding. Adevice driver, associated with the VP board 122, is loaded into systemmemory during load operations. The device driver supports communicationsbetween the VP board 122 and the application code at run time (e.g.,when a person is seeking medical advice). Through a shared memorysegment, the device driver sends event and status data to theapplication code in real-time as events occur on the associatedtelephone line. These events include the ring of an incoming call, touchtone key pressed by the caller, and the hang-up signal. The VP board 122plays back speech messages that are stored on the hard drive 152. Thealgorithm processor 160 sends a selected speech file having an encodedspeech message that is retrieved from the hard drive 152 to the VP board122 at the appropriate time for speech message playback. A speechmessage can be of variable length with a typical message about one totwo minutes in length. Several speech messages may be chained togetherto produce an extended spoken message, e.g., giving instructions to thepatient. During speech file playback, the VP board 122 is monitoringtouch tone response from the caller. The VP board 122 may be configuredto interrupt speech file playback when a touch tone signal is detected.

System Operating Contexts The system has an activity flag in the portstatus block for each patient currently using the system to keep trackof which state the associated VP board channel is in:

-   -   a. Idle Mode—an idle channel waiting for a telephone call;    -   b. Login Mode—a condition where a patient is in the login        process;    -   c. Registration Mode—a condition where a patient is in the        registration process;    -   d. Real Mode—a condition where a patient is consulting for an        actual medical problem;    -   e. Info (Information) Mode—a condition where a patient is        consulting for information or a hypothetical situation;    -   f. Pause Mode—a patient-initiated pause condition;    -   g. Pending Mode—similar to Real mode except that new medical        information gathered for a patient is not automatically added to        the patient's medical record, but rather written to a “Pending”        file where it will be verified off-line by a staff person.

Voice Keywords and DTMF Command Keys The system is responsive to thefollowing voice keywords and DTMF keys when it is in a prompting state,i.e., not in response to a menu message:

DTMF Voice yes 1 Useful for answering yes/no questions. no 2 backup #Causes the system to back up to the “predecessor” message (see below),then resume playback help * Plays helpful information: either the node'shelp message list, or the DTMF command explanation message. operator 0Causes the system to transfer the caller to a live person. pause 7Transitions to pause mode. The system default pause period is 30seconds. quit 9 Quits the current algorithm, and takes the caller tonode 110, which asks the caller if (s)he wishes to select anotheralgorithm. repeat 3 Repeats the current node's play message list. Ifthis command is given in the middle of a long play list, then playbackrestarts with the first message in the list. Pause Mode Commands yes 1Extends the pause period by one default pause interval (30 seconds).continue 2 Ends pause mode. If this occurs at a Yes/No node, the systemwill repeat the question. If this occurs at a Link node, the system willresume playback with the “current” message. The system resolves the DTMFdigit “2” ambiguity, “no” versus “continue”, by examining the pause modeflag.

FIG. 2 illustrates how speech files are created. A person programmingmedical algorithms uses speech messages to communicate with the personseeking medical advice. As previously mentioned, these speech messagesare of variable length. The programmer typically writes a script for thespeech message. Then using the handset of the telephone 110, aspeakerphone feature, or other voice-input device, e.g., a microphone,the programmer reads the script into the voice-input device which isconnected to the VP board 122. The VP board converts the speech into adigital format and records the digitized speech to a file that is storedon the hard drive 152. In the presently preferred embodiment, asubdirectory named vox contains the system speech files, andsubdirectories for each medical algorithm. System speech files are ofthe form sysxxx, where xxx is some arbitrarily assigned number. Thesystem messages are used by the “fixed” parts of the system, e.g.,greeting, login process, registration process. There are a few speechfiles of the form msgxxx. These are the past medical historyquestionnaire messages, and response acknowledgements. There areadditional speech files of the form msgxxxx in each of algorithmsubdirectories, where xxxx generally matches the node number, which willbe explained hereinbelow. Node messages include information, question,menu and help messages.

III. Operating Features of the MDATA System

One of the MDATA system's main objectives is to bring togetherhighly-qualified medical experts, encode their knowledge in a centrallocation, and make it available to everyone. A new and unique authoringlanguage is used by the MDATA system to help accomplish this objective.

Each day, specialists perform the same tasks over and over. They enactthe same diagnostic ritual of solving a familiar problem. At the sametime, however, primary care physicians attempt to find the best paththrough the diagnostic maze of an unfamiliar problem. This process isinefficient and fraught with error.

In medicine, there is generally one best way to do things. Instead ofphysicians spending valuable time duplicating tasks, the MDATA systemutilizes medical experts from each medical specialty who write detailedalgorithms for the treatment of the 100 or so most commonly encounteredcomplaints in family practice and emergency medicine. These algorithmsare carefully and specifically designed to elicit historical data andphysical findings over the telephone, rather than in face-to-faceinteractions.

Several experts could work together to thoroughly research oneparticular complaint as well as to anticipate the full spectrum ofpossible problems and patient responses. These experts could alsoprovide and maintain the MDATA system treatment table as well as theimaging modality of choice and laboratory test of choice tables. Theseconcepts will be described hereinbelow.

Carefully crafted questions, used in the taking of a medical history,are the main tools that the MDATA system uses to assess the problems ofpatients. The key to getting a good history is to ask the rightquestions. In a sense, in the diagnostic process questions are liketests. It is important to note that the right questions are basicallyalways right; they don't change. Although they may be refined over time,in general, once excellent and well-crafted questions are developed theyare good for a very long time. Of course, as new diseases arediscovered, e.g., toxic shock syndrome and AIDS, new sets of diagnosticquestions are developed that are disease specific.

The questions used by an earlier generation of physicians, who did nothave any of the latest imaging modalities (types or methods), are farmore sensitive and precise in diagnosing a patient's problem than thequestions used by doctors today. The MDATA system makes use of finenuances of language to diagnose patients as well as to determine whencertain tests or imaging studies are necessary.

The MDATA system's statistic generating capabilities enable the systemto analyze the effectiveness of the questions used in the diagnosticprocess. As a result, physicians benefit from the immense amount ofstatistical information that is gathered regarding the wording ofquestions asked in taking medical histories. For example, exactly whatpercentage of patients who answer “yes” to the question, “Is this theworst headache of your life?” actually have a subarachnoid hemorrhage?Although this is a classic description of this problem, the exactprobability of having this kind of brain hemorrhage after answering“yes” to this question is not presently known.

Currently, doctors can only estimate the probability of certainconditions based on history. By applying the statistical informationthat is generated, the MDATA system not only provides the patient withadvice that is continually improving, but it will also be able to passalong these probabilities to the entire medical community.

To function optimally, the MDATA system tries to gain as much medicalinformation about its patients as possible. Although a first-time calleris given excellent advice, more specific advice can be given if thesystem has more information. Therefore, the MDATA system asks patientsfor their complete medical history. The MDATA system can either obtainthe patient's medical record over the telephone or it can mail or fax adetailed questionnaire to each patient. The patient can then gather thenecessary information at their convenience. The MDATA system will alwaysbe available by telephone to clarify any questions the patient may have.

The MDATA system uses the “International Classification of Diseases”(ICD-9-CM) codes to help summarize the information it has about apatient. This world standard is a comprehensive numerical system used toclassify the entire spectrum of medical diseases. ICD-9-CM codes arealso used to classify specific procedures performed (e.g., appendectomy)as well as the morphology of neoplasm (i.e., tissue diagnosis of acancer).

In addition, the MDATA system 100 uses ICD-9-CM “E-Codes” to classifyenvironmental events, circumstances, and conditions as the cause ofinjury, poisoning, and other adverse effects. These codes areparticularly helpful for storing information about what drugs thepatient has taken or is currently taking, as well as the context (e.g.,therapeutic use, accident, poisoning, suicide attempt) in which theywere or are being taken. For example, E942.1 is the code for thetherapeutic use of digoxin. Medications are also cross-categorizedaccording to the classification done by the American Hospital FormularyService List (AHFS) Numbers. The MDATA system 100 also uses “V-Codes” toclassify other types of circumstances or events such as vaccinations,potential health hazards related to personal and family history, andexposure to toxic chemicals.

It is estimated that the alphanumeric component of a patient's medicalhistory will not exceed 1,000 atoms or pieces of information. An atom isconsidered herein to be a separate identifiable data item or point. Withthis assumption, the medical records of every person on the planet couldcurrently be stored on approximately 1,000 optical disks.

While a patient interacts with the MDATA system, the system isconstantly determining what questions to ask, based upon the informationit has about the patient. Just as a physician gathers relevant pieces ofinformation from his or her dialogue with a patient, the MDATA systemflags and later stores all pertinent pieces of information that itlearns from each interaction with its patient. Therefore, certainquestions, because their answers remain the same, need not be repeated.For example, if the MDATA system learns that a patient's mother hassuffered from migraine headaches, it will never have to ask for thisinformation again.

Again, the more information the MDATA system has about a patient, themore specific is its advice. It is not uncommon for the MDATA system togive different advice to different patients calling for the samecomplaint. In other words, the advice given is patient-specific. Notonly can the MDATA system's advice be different for different patients,but there are times when the advice given to the same patient (callingfor the same complaint but at different times) is different. Forexample, one of a group of functions called “meta” keeps track of thenumber of times the MDATA system has been consulted for the sameproblem. Once a threshold is reached, the MDATA system advises thepatient that the number of consultations alone, for the same complaint,may signify a problem. The system then makes an appropriaterecommendation.

Before the MDATA system stores any information, the system verifies itsaccuracy. To accomplish this task, “confirmation loops” are used. Anypiece of information that will become a part of the patient's medicalrecord is sent through a confirmation loop where the system asks thepatient to verify the accuracy of the information that the system hascollected. The confirmation loop enables the system to verify newpatient information and make corrections before it enters thisinformation into the patient's medical record.

IV. Authoring Language

The MDATA system uses a new authoring language that is specificallydesigned to allow medical knowledge to be encoded into a usable computerprogram. The presently preferred voice response or telephony version ofthe MDATA system is written in object-oriented Microsoft C\C++ version7.0. This allows the MDATA system to easily interface withindustry-standard database programs, including those that are SQL-based,as well as to be portable to other operating systems. The operatingsystem is transparent to the user.

Before the development of the MDATA system's authoring language, therewas no practical way for medical experts to encode their knowledge intoa meaningful, useful, and accessible structure. Although other computerlanguages have been used to build medical expert systems, they havealmost always required a knowledge engineer and a programmer to beinvolved. Quite often, the knowledge encoded in these systems could onlybe accessed and fully understood by physicians. Typically, theprogrammer would try to translate the doctor's diagnostic skills andtreatment rules into computer code. This separation of the physician'sknowledge from the encoded treatment recommendations often engenderedanxiety in the physician and has, at times, led to inaccurate treatmentrecommendations.

The MDATA system's authoring language, however, is designed to allowphysicians to transfer their knowledge into a computer program that canbe directly accessed by non-medically trained personnel. Recursive anditerative techniques are used to acquire the knowledge from the expertand assemble it in a way that allows it to be immediately transposedinto the MDATA system's algorithms. Because of the simple interface ofthe language, and because a formula for writing the algorithms hasalready been developed, physicians who are not computer literate canencode their knowledge as well as understand exactly how that processtakes place.

The MDATA system's authoring language allows flat information to berestructured into a hierarchical or layered format in which thearrangement of the knowledge conveys meaning. Thus, a textbookdescription of a disease can be transposed into a form that allowsuseful treatment recommendations to be made.

The new language also allows the formation of a structure in whichmultiple overlays of screening questions, combined with the applicationof recursive techniques, sequentially exclude some diagnoses while atthe same time reaching treatment recommendations. The MDATA system'ssimplicity and elegance would not be possible without the new language.

The MDATA system's authoring language allows an algorithm programmer toretrieve information from a patient's medical record, request additionalinformation from the patient, and guide the flow of algorithm executionbased on medical history and the patient's responses. The languageallows the programmer to implement an algorithm in a natural scriptedstyle.

The course of an algorithm is determined by caller responses toquestions that the MDATA system asks. For simple “yes/no” questions, theflow of interaction can be described by a binary tree. Multiple-choicequestions (e.g., menus) provide multiple branches in the tree. Eachquestion can be considered a node, and the acceptable responses to thisquestion are branches leading to the next question (node). Using thisabstraction of an algorithm, one can draw a directed graph (also knownas a node map) of the nodes and branches of an algorithm, beginning withthe initial question, and ending with all possible terminal points.

The node table is built in this manner:

-   -   1. An author develops an algorithm.    -   2. The algorithm is broken up into separate nodes.    -   3. A directed graph is drawn up, which is a flow chart of the        algorithm's operation.    -   4. Each node's definition is entered into the MDATA system,        either by:        -   a. using an ednode utility to write each node's definition            into the system's machine readable node table, or        -   b. using an xlat utility to translate an ASCII file of            human-readable node definitions into the system's machine            readable node table.

Referring to FIG. 3, a process for translating a medical algorithmwritten in the authoring language will be described. FIG. 3 illustratesan ASCII (American Standard Code for Information Interchange) formattext file 170 as an input to a translation utility 172. An ASCII filecan be created by use of a text editor or a word processing program (mayneed to export to the ASCII format). The ASCII file 170 contains nodedefinitions conforming to the syntax briefly described hereinbelow.

The purpose of the ASCII node definition translator utility 172(xlat.exe, along with functions in pars.c and util.c) is to convert ahuman-readable document into a machine readable format that the MDATAsystem reads at run time to process an algorithm. This utility 172 maybe considered to be a preprocessor; the translation must be accomplishedprior to run time.

The output of the utility 172 is a set of binary (NOD_BLK) recordswritten to a node table 174 (filename of node.fos), and a set of binarylist files 176 (in a subdirectory \list\listxx\xxyy, where xx is thefirst two digits of the node number, and yy are the last two digits).Four list files 176 a-176 d are shown as an example. Each “list” file,e.g., 176 a, contains a “next” table (i.e., the ‘next node after thisone’), a message play list for this node, and a “work” list (i.e., oneor more “things to do” at this node before beginning speech playback).The binary record written to the node table 174 (node.fos) has fieldscontaining the node number (which is redundant; the record's position inthis file also indicates the node number), the node's “type” attribute(Menu, Link, Prompt, Yes/No, Return, Hangup) and a parent node number.

The node table 174 is a table of 10,000 NOD_BLK records. This table 174is indexed by a node number, e.g., the fiftieth record corresponds tonode 50. The contents of the individual node records may be viewed byselecting “Display Node” while running the ednode utility. The noderecords are modified by either using the ednode utility, or whentranslating node definitions from ASCII to the node file with the xlatutility.

One of the following keywords is necessary as the first item on eachline, but only one keyword is accepted per line; any excess informationwill be discarded.

-   -   Node The Node keyword denotes the beginning of a new node and        defines the node number.    -   Parent The Parent keyword defines the parent of the node being        defined.    -   Type The Type keyword defines the class of the node being        defined. Acceptable type names are:        -   Menu This node presents a multiple choice question.        -   YesNo This node presents a simple Yes/No type question.        -   Link No caller response is required at this node, algorithm            processing will continue at a predetermined node.        -   Prompt This node requests some numeric information from the            caller. The information is placed in a DTMF buffer which is            then stored in the next node.        -   Return Returns from a subroutine call (e.g., after            configuring a past medical history object).        -   Hangup The system will release this caller after it finishes            speech file playback, or if the caller interrupts playback            with a DTMF key press.        -   Wait nn This node will play the message list, then pause for            the specified nonzero number of seconds before continuing.    -   @ The @ keyword defines the action to be taken for a response to        either a Menu or YesNo type node.    -   Digits The Digits keyword is used in conjunction with Type        Prompt to indicate the maximum number of DTMF digits to collect        from the caller.    -   Play The Play keyword defines a play list of one or more        messages to be played at this node.    -   Help The Help keyword defines a play list of one or more        messages containing useful hints for interacting with the        system. These messages provide helpful instructions for a new or        confused caller.    -   Next The Next keyword defines the next node to jump to after the        node being defined. It is used in conjunction with node types        Link and Prompt.    -   Work The Work keyword indicates a sequence of one or more        operations to perform when arriving at the node being defined.        This processing occurs before speech playback begins.

A select set of math functions, relational operators, and nestedif-then-else statements are supported. A pound sign (‘#’) or a hyphen(‘-’) in the first position on a new line will cause the translator toskip over the rest of the line. This is useful for inserting comments,or delimiting between individual node definitions. The translator alsodisregards blank lines.

In order for a node to be properly defined, a minimum number of keywordsmust be present for each node, and other keywords must be includeddepending on the node type. The minimum keyword set for a properlydefined node is:

-   -   Node, Parent, Type, and Play.        -   Dependency rules:            -   (1) The Menu type requires at least an @ 1 line and an @                2 line.            -   (2) The YesNo type requires an @ 1 and an @ 2 line (@ 3,                etc. are ignored).            -   (3) The Link type requires a Next line.            -   (4) The Prompt type requires a Digits line and a Next                line.

The first keyword in a node definition must be Node. The other keywordsmay be given in any order. The next occurrence of the Node keyword willinvoke a completeness test. If the completeness test is successful, thenthe node definition is saved in machine readable (binary) format, andtranslation continues with the new Node line. A set of reserved languagekeywords is listed in Table 1.

TABLE 1 Reserved language keywords (case insensitive): @ and digbufdigits else essf flush hangup help if keep link menu meta next nodeparent play pop prompt push reenter return test then type wait writework xor yesno

V. Run-Time Operation

Referring to FIG. 4, the run time interaction among the hardware andsoftware components of the MDATA system 100 will be described. Aspreviously mentioned, algorithm processor 160 includes the parser andsupporting functions that manipulate the memory variable symbol tableand the run time stack. For a selected medical algorithm, a node recordis read from the node table 174 and a list file is read from theplurality of list files 176. The algorithm processor also interacts withthe Vpro voice recognition (VR) board 124 for speech recognition andwith the Dialogic voice processing (VP) board 122 for speech playbackand DTMF detection. The VP board 122 further is interconnected with aset of speech files 180 that are stored on a portion of hard disk 152and with one of the telephone lines 106 that connects via the telephonenetwork 108 (FIG. 1) to the patient's telephone 110. The VR board 124further connects with the voice print vocabularies 182, previouslydescribed, also stored on a portion of hard disk 152. The algorithmprocessor 160 utilizes the speech recognition, speech playback, and DTMFdetection resources as directed by the medical algorithm that isretrieved from the node table 174 and the list files 176.

Referring to FIGS. 4, 5 a and 5 b, several data structures are utilizedat run time. These data structures are described as follows:

-   -   A. Port Status Block (PSB). A port status block is created at        run time for each VP board 122 channel. The PSB contains flags,        buffers and tables that hold the state information of the        channel, retain responses from the caller, and keep track of        where to transfer control in response to voice recognition and        telephony events. The PSB keeps track of whether the caller        prefers to use spoken or touch tone responses, the caller's last        response, the number of consecutive errors the caller has made,        and other context sensitive parameters.    -   B. Node Block. This structure 196 contains the node number, the        type attribute (link, menu, yes/no, hangup, prompt, wait,        return) and pointers to:        -   a. Help list—a Play list of help information;        -   b. Play or Message list—a list of one or more messages or            speech files to play in sequence at each node;        -   c. Next table or list—contains entries for each possible            response to a yes/no or menu node that are evaluated at run            time to determine the next node to branch to; and        -   d. Work list—things to do before message playback starts.    -    The load_node() routine 194 in util.c builds the node block        structure 196 in memory by first reading in a node record 190        from the node table 174. Then linked lists are attached to the        pointers help, play, next and work. These lists come from the        list files 176, in subdirectory path \list\listxx\xxyy, where        xxyy is the node number, wherein each list file 192 is        associated with a unique node.    -   C. Symbol Table. Each patient has their own associated symbol        table. A portion of a symbol table 212 is shown in FIG. 5 b. The        symbol table is loaded at run time with memory variables that        hold patient specific data (age, sex, and items from medical        history) and algorithm specific data. The items in the symbol        table can be flagged for storage to the patient's medical        history.    -   D. Run Time Stack (RTS). Each Dialogic VP board 122 channel has        a RTS associated with it. The RTS is used by the parser. The        algorithm programmer can push to and pop from the RTS, e.g., to        temporarily store a value of a variable.

The work list has the non-playback tasks that are performed at eachnode. There is one work list for each node, and it is identified withthe work keyword in the ASCII node definition file. The work list may beempty. Each time the system transits to a new node, it will execute thework list. If the patient repeats a node, the system will not executethe work list again; it will simply replay the message(s). If thepatient requests the system 100 to back up the node map, the system willexecute the work list of the node it backs up to. Typical tasks in thework list involve manipulating objects on the run time stack or in thesymbol table, testing for the presence of memory variables, configuringpast medical history or current medical condition objects, or writingdatabase records. An example of a complex work list follows:

“Test OBJECT2; Phone=DIGBUF; Push Age”

-   -   This example tests for the presence of a patient record object        labeled “OBJECT2”, loads the contents of the digit buffer into        memory variable Phone, and pushes the value of memory variable        Age onto the run time stack.

Each node has the “next” table or list. The next list indices range from1 to 9, inclusive. The next list contains either a single node number,or an if expression. For all node types, except the Hangup node, therewill be at least one next list:

-   -   Link and Prompt nodes: the next node is stored at table index 1.    -   Yes/No node: the next node for the Yes response is stored at        table index 1, and the No response is stored at index 2. This        corresponds to the prompt, “if the answer is yes, press 1; if        no, press 2.”    -   Menu node: the response number and the table index are the same.        Even though the actual data structure has a ‘0’ index in the C        programming language, this index is not used in the next table        because a ‘0’ response is reserved for operator assistance.    -   Following is an example of a next list:        -   “If Male and Age >55 then 100 else 200” is interpreted as:        -   If the patient is both male and over 55 years old then go to            node 100 else go to node 200.

Speech files 180 may be of an arbitrary length. A message may beinformational, a list of menu options, or a yes/no question. A “twoparagraph” or “under one minute” limit has been adopted as a styleconvention for the presently preferred embodiment. Typically, a node isprogrammed as a sequence of Yes/No nodes, with “informational” Linknodes interspersed as needed. When there is a lengthy discussion, thespeech is recorded in multiple files. To simplify algorithm programmingand enhance readability (viz., eliminate long chains of link nodes), theLink node's play list may contain up to ten message numbers.

Upon arrival at a Link node, the system positions a “current message”pointer at the beginning of the play list (trivial case: single messageplay list; interesting case: multiple message play list). As playbackproceeds, the current message pointer moves down the play list. Afterthe system plays the last message on the list, it moves on to the nextnode.

If the caller issues a “backup” command, the system will move thecurrent message pointer back one message, and resume playback. If thepointer was at the beginning of the list (e.g., trivial case), thesystem backs up to the previous node and places the current messagepointer at the beginning of the play list. If there is more than onemessage in the list, the system cues the pointer to the last message inthe list. The system then resumes playback. In the “pause” mode, whenthe caller issues the “continue” command, the system will resumeplayback at the current message.

The MDATA system 100 uses three basic operating modes:

-   -   A. Real Mode—involves an actual medical problem. In this mode        the system 100 loads the past medical history, saves new past        medical history objects, and writes a meta record for each        algorithm consulted. The medical algorithm programmer is        responsible for providing code to jump past meta analysis in        Information mode.    -   B. Information Mode—involves a “what if” scenario. In the        Information mode the system 100 disregards past medical history,        does not save newly configured past medical history objects,        does not write a meta record for each algorithm consulted, and        does not perform meta analysis. The patient has an option in        Information mode to change the age and sex parameters to emulate        a hypothetical patient.    -   C. Pending Mode—handles the situation when a patient's voice        sample does not match the patient's reference sample. Pending        mode is utilized also when an assistant is interacting with the        MDATA system 100 on behalf of a patient and both the assistant's        and the patient's voice samples fail the voice printing test. In        the case where the assistant's voice sample fails the voice        printing test but the patient's voice sample passes the test,        Pending mode is not utilized. In Pending mode, the MDATA system        100 considers the patient's medical history and performs meta        analysis during this consultation. However, a meta record is not        written for this consultation and any new medical information        gathered on this patient will not be written to the patient's        medical record. The new medical information is written to a        “Pending” file. The Pending file is verified off-line by a        system administrator or staff person, and then is added to the        patient's medical record only if the information can be        verified.

One of the drawbacks of the traditional doctor-patient relationship isthe short amount of time that physicians are able to spend withpatients. The MDATA system 100, however, allows patients as much time asthey wish to learn about their problem as well as to obtain informationon any number of other medical topics.

Through the “Information mode” feature of the MDATA system 100, callerscan learn about a disease process, an illness or the latest treatmentfor any disease, without adding any information to their personalmedical record. Although the system 100 keeps track of the interaction,it is labeled as an “Information mode session.” The record of thecaller's path through the system is not used as the basis for any futureadvice, nor is it considered in generating system statistics.

The Information mode is not limited to complaints for which the MDATAsystem 100 offers medical advice. Information about early detection andtreatment of many other diseases as well as the latest advances inmedicine can be made available through the Information mode.

Referring to FIGS. 5 b through 5 g, as an example, a run time sequenceof steps of how a patient may traverse a main menu node map severalsteps into a chest pain algorithm node map will be described. Six nodeswith a portion of an associated symbol table will be discussed.

At FIG. 5 b, the algorithm processor 160 loads the first node #100,represented by node block 210. The variables for Age, Sex, and Real modewere loaded into the symbol table 212 during the login process (whichwill be described hereinbelow). Throughout this example, the help listis empty, i.e., no help information is played for the patient. The worklist sets the Problem variable of the symbol table 212 to be Menu. Thenthe system 100 begins playback of message#100. This message gives thepatient a menu of choices to choose from. The Digits entry equal to onemeans that a one digit response is expected from the patient. Thepatient may respond by pressing a touch tone (DTMF) key on the telephoneor speak the choice response into the telephone handset microphone. Inthis example, the patient selects menu option “1”. The parser evaluatesthe Next list based on the patient selection and branches to node #101.

At FIG. 5 c, the algorithm processor 160 loads node #101, represented bynode block 214. The work list is empty, so the system 100 goes right toplaying back message#101 which presents another menu of choices to theuser. The Next list has four nodes for possible branch points. In thisexample, the patient selects menu option “1” for a chest pain complaint.The parser evaluates the Next list based on the patient selection andbranches to node #2200.

At FIG. 5 d, the algorithm processor 160 loads node #2200, representedby node block 218. The work list command is to update the value ofProblem in symbol table 220 to CCHP (chest pain). Then the system 100begins playback of message#2200. No response is required from thepatient for a Link type node. The Next list has two nodes for possiblebranch points depending on the value of symbol table variable Real. Theparser evaluates the If expression in the Next list for the value ofReal and, in this example, branches to node #2201.

At FIG. 5 e, the algorithm processor 160 loads node #2201, representedby node block 222. The work list command is to write a Meta consultationrecord for future use by a Meta function. The play list is empty so nomessage is played. No response is required from the patient for a Linktype node. The main purpose of this node is to write the Metaconsultation record (because the system is currently in Real mode forthis patient). The Next list has only one node so no decisions arenecessary by the parser which, in this example, branches to node #2205.

At FIG. 5 f, the algorithm processor 160 loads node #2205, representedby node block 226. The work list is empty in this node so the system 100goes right to playing back message#2205 which presents a yes/no type ofquestion to the user. The Next list has two nodes for possible branchpoints depending on the response of the patient. In this example, thepatient responds “no”, and the parser evaluates the Next list based onthe patient selection and branches to node #2210.

At FIG. 5 g, the algorithm processor 160 loads node #2210, representedby node block 230. The work list is empty in this node so the system 100goes right to playing back message#2210 which presents a yes/no type ofquestion to the user. The Next list has two nodes for possible branchpoints depending on the response of the patient. If the patient answers“yes” to the question, the parser branches to node #2211, but if thepatient answers “no” to the question, the parser branches to node #2215.

VI. Software Structure

Referring to FIG. 6, the system utilizes eight principal, separateprocesses and seven related databases. A patient login process 250 isused by the system 100 to identify a patient who has previouslyregistered into the system by prompting for a patient identificationnumber (PIN). An assistant login process 272 is used by the system 100to identify an assistant who has previously registered into the systemby prompting for an assistant identification number (AIN). An assistedpatient login process 276 is used by the system 100 to identify apatient who has previously registered into the system by prompting forthe patient identification number. If the caller is the patient, apatient registration process 252 is used by the system to register newor first-time patients. If the caller is not the patient, an assistantregistration process 274 is used by the system to register new orfirst-time assistants. Then, if the patient is not already registered,an assisted patient registration process 278 is used by the system toregister the patient. These processes will be further describedhereinbelow.

Once a caller has logged in or registered, the system provides a choiceof two other processes in the current embodiment. The first of theseprocesses is the evaluation process 254 that performs a patientdiagnosis. The second of these is a treatment table process 256 toobtain current treatment information for a particular disease ordiagnosis. In another embodiment, other choices are added to accessother medical information processes.

Associated with these eight processes are a patient and assistantenrollment database 260, a consultation history database 262, a patientresponse database 264, a medical history objects database 266, a patientmedical history database 268, a pending database 269, and a patientmedication database 270 that are described as follows:

-   -   A. The master patient and assistant enrollment database 260 is        created at run-time by one of the registration processes 252,        274, or 278. This database 260 is read by the patient login        process 250 or the assisted patient login process 276 to        validate a patient's identity at login time and by the assistant        login process 272 to validate an assistant's identity at login        time. The database 260 is essentially a master file of all        registered patients and assistants indexed by their patient ID        number or assistant ID number, respectively. The patient ID or        assistant ID, date of birth and gender fields are entered by the        on-line registration process; the system administrator manually        enters the name of the patient or assistant in an off-line        manner.    -    The patient and assistant database 260 contains one record for        each patient or assistant. This database 260 is indexed by the        identification number. The system appends the enrollment        database 260 after a caller is successfully registered. The        “next ID number” is stored in a binary file, config.fos, and is        incremented after each successful registration. Each record has        the following fields:

Field Name Data Type Width Usage ID Numeric 10 ID number TYPE Character1 User type: “P” - patient, “A” - assistant ASST_PERM Boolean 1Permanent assistant flag ASST_EXP Date 8 Expiration for permanentassistant RELATIONS Pointer 20 Pointers to related patients/ assistantsORGZTN Character 8 Organization alphanumeric code NAME Character 20Patient/Assistant name SEX Character 1 Gender YEAR Numeric 4 Year ofbirth MONTH Numeric 2 Month of birth DAY Numeric 2 Day of birth ACCESSDate 8 Last access RV_PATH Character 20 Path name of recorded voice file

-   -   B. The consultation history or meta database 262 is created at        run-time by the evaluation process 254. A consultation record        contains alpha-numeric codes for the patient's complaint, the        affected anatomic system and the diagnosed cause of the        patient's complaint. When the meta function is invoked at        run-time, it compares alphanumeric strings provided by the        evaluation process with the fields of all the patient's meta        records that fall within a time window specified by the        evaluation process. The meta function returns the number of        matches found, and an indication of the frequency of the        patient's complaint.    -    Each patient has an individual meta file that is part of the        consultation history database 262. At the conclusion of the        evaluation process and dependent on the run-time operating mode        flag, the system will create a new meta record, populate its        fields with the information gathered during the evaluation        process, and append this record to either the consultation        history database 262 or the Pending file 269. For example,        information used in the new meta record may come from a “Write        Meta” command in a node Work list. Each record has the following        fields:

Field Name Data Type Width Usage DATE Date 8 Date stamp PROBLEMCharacter 5 Patient complaint/symptom SYSTEM Character 5 Anatomicalsystem affected CAUSE Character 5 Diagnosed cause of complaint

-   -   C. The patient response database 264 is created at run-time by        the evaluation process 254. The response database 264 is an        audit trail: each record is time stamped and registers the        patient's response to each question. This database 264 can later        be analyzed off-line with a database program such as        FoxPro/FoxBase to reveal how the patient responded to questions        during the evaluation process 254, or a database program can be        developed to gather response patterns and statistics and        generate appropriate reports.    -    Each patient has a response trace file that is part of the        patient response database 264. The system 100 appends this        response trace file with a response record every time the        patient answers a question or provides algorithm-requested data.        For human readability, the system also inserts “Begin Call” and        “End of Call” records in this file. Each record has the        following fields:

Field Name Data Type Width Usage DATE Date 8 Date stamp MM/DD/YY TIMECharacter 8 Time stamp HH:MM:SS NODE Numeric 6 Current node number TYPECharacter 5 Response type: DTMF or VOICE RESP Character 5 Responsecommand or digit string MODE Character 1 Consultation operating contextVERSION Character 20 Version or Begin/End call comment SENS_FACTCharacter 20 Current sensitivity factor settings

-   -   D. The medical history objects database 266 is an auxiliary        database that supports a key feature of the MDATA system 100:        past medical history. The medical history objects database is a        catalog of unique alphanumeric codes, each code corresponding to        a medical condition or diagnosis that is not expected to change        during the life of the patient (e.g., a diagnosis for asthma is        coded as “RWHZAST”).    -    In addition to the alphanumeric codes, the MDATA system 100        uses the “memo” field in a Foxpro database to store binary        objects. Currently, these binary objects are clinical sounds        obtained from the patient over the telephone.    -    It is anticipated, that as database technology gets more        sophisticated (moving toward multi-media and so forth), it will        allow storing of larger and more complicated binary files such        as the following: a digitized x-ray, a digitized CAT scan, a        digitized MRI scan. In addition, as video-telephone technology        advances, it is anticipated that the system 100 will store video        images or even holographic images of the patient.    -    For every past medical condition there is a record in the        medical history objects database that contains the attributes of        the medical condition, and contains a pointer into the past        medical history questionnaire. The attributes of a medical        condition include its data type (e.g., Boolean or numeric) and        the number of digit positions needed to store the value of a        numeric value associated with this condition (not applicable to        Boolean type).    -    The pointer field is useful for obtaining medical history at        run-time. If a patient has an incomplete medical history        questionnaire on file with the MDATA system 100, then the        pointer field allows the evaluation process to momentarily        suspend the evaluation, go to the medical questionnaire and ask        an individual question, collect and verify the patient's        response, and then resume the evaluation process. This        “ask-when-you-need-it” approach relieves the new patient of        going through an exhaustive medical history questionnaire before        the first consultation of the diagnostic process.    -    Each record of the medical history objects database has the        following fields:

Field Name Data Type Width Usage LABEL Character 8 Object code name TYPECharacter 1 Object data type DIGITS Numeric 3 Maximum number of digitsin response CALL Pointer 6 Identifies question(s) to be asked toconfigure this object AUDIO Binary N/A Voice print IMAGERY Binary N/AFace print RFU Character 20  (For future use)

-   -   E. The patient medical history (PMH) database 268 is created at        run-time by the evaluation process 254 or by use of a past        medical history questionnaire. The PMH database 268 is read by        the evaluation process during run-time. This database 268        contains each patient's individual medical history. A new        patient has an option to go through the entire medical        questionnaire at one time, thereby configuring all the past        medical history objects listed in the objects database 266.        Alternately, the new patient can bypass the questionnaire and go        right into the diagnosis of a medical complaint. Then, if a        medical algorithm requires a past medical history object that        has not yet been configured, the evaluation process 254 invokes        a past medical history function before it continues with the        algorithm.    -    Each patient has their own past medical history file, which is        part of the PMH database 268, that contains records which        describe medical events or conditions from the patient's life.        The system 100 appends a record to this file each time a past        medical history object is configured for the patient. The        contents of this file are installed in the symbol table when the        patient logs in to the system 100. The medical algorithm        programmer is responsible for using a TEST command to verify        that necessary items are present in the symbol table before        algorithm execution. A side effect of a negative TEST result is        that the system 100 prompts the patient to provide that        information. The system 100 flags any new or modified items, and        asks the patient to confirm these values during an Exit        Confirmation Loop which will be described hereinbelow. Each        record has the following fields:

Field Name Data Type Width Usage LABEL Character 20  The object's labelTYPE Character 1 Object data type VALUE Character 10  Object'sconfigured value CERT Numeric 3 Certainty of object's value DATE Date 8Object configuration date ICD9A Float 5 First ICD-9 code ↓ ↓ ↓ ↓ ICD9EFloat 5 Fifth ICD-9 code

-   -   F. The “Pending” database file 269 holds medical information        gathered during Pending mode for offline verification. The        Pending database record structure is the same as that used for        the past medical history (PMH) database 268. The evaluation        process writes to the Pending database at run-time when it        configures a new past medical history object for a patient        during a Pending mode interaction. The contents of the Pending        database are reviewed off-line by a staff person, and if the        information is verified, the staff person appends the        information to the patient's past medical history file.    -   G. An optional patient medication database 270 contains a file        for each patient containing information about medication they        are taking, or have taken in the past. The medication database        270 is created by the evaluation process 254 at run time. A        “Write Drug” command builds a record and fills its fields with        same-named memory variables from the symbol table. The        evaluation process 254 may read the medication database 270        during run time as needed. The treatment table 256 optionally        reads the medication database 270 to determine the medication(s)        being used by the patient.

Field Name Data Type Width GENERIC_NAME Character 20 TRADE_NAME1Character 20 TRADE_NAME2 Character 20 TRADE_NAME3 Character 20ICD-9-CM_CODE Character 10 ICD-9-CM_ECODE Character 10 ICD-9-CM_VCODECharacter 10 OTHER Character 20 DOSAGE Character 20ROUTE_OF_ADMINISTRATION Character 10 FREQUENCY Character 10 USECharacter 20 START_DATE Date 8 STOP_DATE Date 8 OTHER1 Character 20OTHER2 Character 20

VII. Top-Level Flow

Referring to FIGS. 7 a, 7 b, 7 c and 7 d, the top level flow 300 of theMDATA system 100 software will be described. The telephone number usedto access the MDATA system 100 may vary in various embodiments of thesystem. If the sponsoring agency or hospital wishes to provide access tothe MDATA system 100 at no cost to the caller, then a toll-free 800service number can be used. If the sponsoring agency or hospital wishesto recover the costs of running the MDATA system 100 from the caller, itmay use a pay-per-call or premium charge number (e.g., 900 service).“Current Procedural Terminology” (CPT-4) codes are available to describeand bill third party payers for telephone consultations. They are alisting of the descriptive terms and identifying codes for reportingmedical services and procedures. CPT-4 codes are the most widelyaccepted nomenclature for reporting physician services to insurancecompanies.

Beginning at a start state 302, a person 112 (FIG. 1) desiring medicaladvice calls the telephone number for the MDATA system 100 on atelephone line 106. The caller may be the patient or may be an“assistant”, e.g., parent, relative, or friend, that is helping thepatient. Moving to state 304, the system 100 answers the callautomatically and greets the caller 112 with an introductory greetingmessage by playing back a speech file stored on the hard drive 152 byuse of the VP board 122. Proceeding at state 306, the MDATA system 100asks each patient who calls the system a series of “initial screeningquestions.” These questions are designed to identify patients who arecritically ill; they are not designed to identify the patient's problem.The initial screening questions enable the system to filter out patientswho require immediate medical attention.

Moving to decision state 308, any patient found to be critically ill isinstructed to dial the emergency response telephone number “911” atstate 309 or will be automatically connected to the nearest emergencymedical services system in the patient's area. The telephone call isterminated by the computer 102 at state 310. The following are examplesof initial screening questions:

-   -   IS THIS A MEDICAL EMERGENCY?    -   ARE YOU HAVING DIFFICULTY BREATHING?    -   ARE YOU EXPERIENCING SEVERE PAIN OR PRESSURE IN YOUR CHEST?

If the system determines that the patient is experiencing a medicalemergency, it may provide the patient with a menu of emergency medicalprocedures at state 311. In situations where the patient or the callerfor the patient is far from the nearest emergency help, e.g., a ruralsetting, the caller may need to initiate emergency proceduresimmediately. The menu of emergency medical procedures provides severalchoices to the caller. If the caller presses touch tone key “1” orspeaks the word “one” into the telephone mouthpiece, the computer 102branches to state 312 wherein well known CPR (cardiopulmonaryresuscitation) information is recited. If the caller has a speakerphonecapability associated with the telephone 110 being used, the caller maybe able to listen to and perform the instructions given by the system100 in a hands-free manner away from the telephone. If the callerpresses touch tone key “2” or speaks the word “two” into the telephonemouthpiece, the computer 102 branches to state 313 wherein well knownHeimlich Hug information for choking is recited. At the completion ofeither state 312 or state 313, the telephone call ends at state 314.

If the patient is determined at state 308 not to have a medicalemergency, i.e., the MDATA system 100 is satisfied that no immediatelylife threatening condition is present, the computer 102 moves to adecision state 315 to determine if the caller is the actual patient. Ifso, the computer 102 proceeds to a decision state 316 to determine ifthe patient has previously registered or ever consulted with the system100, i.e., is not a new or first-time caller. If so, the system 100verifies the patient's identification and retrieves their medical recordat the patient login process 250, which will be further describedhereinbelow. At the completion of process 250, the computer 102 proceedsthrough off-page connector C 317 to state 344 (FIG. 7 d). If the patientis not registered, the MDATA system 100 proceeds to the patientregistration process 252 for a new patient, which will be describedhereinbelow. At the completion of process 252, the computer 102 proceedsthrough off-page connector C 317 to state 344 on FIG. 7 d.

If the caller is not the patient, as determined at state 315, thecomputer 102 proceeds through off-page connector A 318 to state 320 onFIG. 7 b. There will be times when the patient may not be able to usethe MDATA system 100 directly, e.g., due to injury, weakness or alteredlevel of consciousness. In these cases, an “assistant” may interact withthe system on behalf of the patient.

An assistant registers with the system through the assistantregistration process 274 which will be described hereinbelow. Theassistant registration record is identical to the patient registrationrecord in structure, but three fields have special significance for anassistant: ASST_PERM, ASST_EXP, and RELATIONS. The ASST_PERM field is aBoolean flag that can only be set true off-line by the systemadministrator who has verified, through separate means, that arelationship exists between a patient and an assistant. Therelationships are one-to-many, i.e., a patient may have one or moreassistants, and an assistant may be related to more than one patient.The ASST_PERM flag may also be constrained by the ASST_EXP field, whichcontains a timestamp for the expiration of the ASST_PERM attribute. Ifthe ASST_PERM flag is true, then the RELATIONS pointers will point toone or more patient records for whom this assistant is a “permanentassistant;” otherwise the RELATIONS field will be empty.

The medical information gathered during an assisted consultation iswritten to the patient's medical record only if the following threeconditions are met:

-   -   (a) the assistant's ASST_PERM flag is True    -   (b) the ASST_EXP timestamp has not been reached    -   (c) the assistant has a relationship pointer to the patient        record        If any of these conditions are not met, then any new medical        information gathered on this patient will be saved to the        Pending file 269 for off-line verification by the system        administrator.

The system 100 establishes at state 315 whether the caller is thepatient, or an assistant. If the caller is not the patient, then thesystem asserts that the caller is an assistant and, at a decision state320, determines if the assistant is registered. If the assistant is notalready registered with the system, the system enrolls the new assistantat the assistant registration process 274. If the assistant is alreadyregistered with the system 100, the computer 102 performs the assistantlogin process 272. At the completion of either process 272 or process274, the computer 102 advances to a decision state 321.

If the patient is not already registered with the system 100, asdetermined at decision state 321, then the system allows the assistantto register a new patient at the assisted patient registration process278. However, if the patient is already registered with the system 100,as determined at state 321, the computer 102 performs the assistedpatient login process 276. At the completion of process 278 or process276, the computer 102 proceeds through off-page connector B 327 to adecision state 334 on FIG. 7 c.

At decision state 334, the computer 102 determines if the patient's dateof birth is in the patient's medical record. If so, the computerproceeds through off-page connector C 317 to state 344 on FIG. 7 d. Ifnot, the system 100 attempts to get the patient's date of birth. Movingto state 335, the system 100 asks the assistant if the patient's date ofbirth is known. If so, the computer 102 advances to state 336 to requestthe patient's date of birth. At state 337, the system 100 recites thepatient's date of birth obtained at state 336. At a decision state 338,the assistant determines if the date of birth is correct as recited bythe system 100. If not, the computer 102 loops back to state 336 torequest the patient's date of birth again. If the patient's date ofbirth is correct, as determined at state 338, the computer 102 flags thedate of birth for saving in the patient's medical record at state 339,and proceeds to state 344 on FIG. 7 d.

If the patient's date of birth is not known, as determined at state 335,the computer 102 proceeds to state 340 wherein the system requests theassistant to provide an approximate age of the patient. The age is animportant parameter used in the evaluation process 254 and treatmenttable 256. At state 341, the system 100 recites the patient'sapproximate age obtained at state 340. At a decision state 342, theassistant determines if the age is correct as recited by the system 100.If not, the computer 102 loops back to state 340 to request thepatient's approximate age again. If the patient's approximate age iscorrect, as determined at state 342, the system 100 advises theassistant at state 343 to get the patient's actual date of birth beforethe next consultation, and proceeds to state 344 on FIG. 7 d. The system100 uses the approximate age in the consultation during the evaluationprocess 254 and the treatment table 256.

At state 344 on FIG. 7 d, the system 100 presents the caller with asystem selection menu. Here, the caller is asked to select from amongfour choices: diagnostic system, treatment table, a futureprocess/function, or end call as described below:

-   -   A. Diagnostic System: The system starts the evaluation process        254 at a menu, where it asks the patient to begin identification        of the complaint.    -   B. Treatment Table: The system takes the patient to the        treatment table process 256 at a menu, where it asks the patient        to select a treatment selection method.    -   C. Future Process/Function: A future process or function 280,        undefined in the present embodiment, that reads and/or writes        the databases shown in FIG. 6.    -   D. End Call: The system performs several steps and then        terminates the telephone call.        In either process 254 or 256, the computer 102 functions as an        interpreter as performed by algorithm processor 160 in following        the node map created by the algorithm programmer. At the exit        point of the evaluation process 254, the system 100 gives the        patient the option of selecting another complaint. At the end of        the treatment table process 256, the system gives the patient        the option of selecting another treatment.

At the completion of the evaluation process 254, treatment table process256, or future process 280, the system 100 loops back to state 344 andrecites the system selection menu to the caller. If the caller choosesthe End Call selection at state 344, the MDATA system 100 moves to adecision state 345. At decision state 345, the system 100 determines ifprocess 254, process 256, or process 280 did not occur in Informationmode, i.e., did occur in either Real mode or Pending Mode, and examinesthe patient's symbol table to determine if any of the configured memoryvariables are past medical history conditions that need to be saved tothe patient's medical history file. If both conditions are true at state345, the system 100 advances to a decision state 346 to determine if theconsultation is being performed in Real mode. If not, the consultationis being performed in Pending mode, and the system 100 then writes anynew patient information obtained during the consultation to the Pendingfile 269. If state 346 proves to be true, i.e., Real mode, for each pastmedical condition that needs to be saved, the MDATA system 100 asks thepatient at state 348 to grant permission to save the datum to thepatient's medical history file and to confirm that the datum is correct.For example, during a consultation for cough, the MDATA system 100learned that the patient has been diagnosed as being HIV positive. Thesystem 100 will ask, “May I record the information about your HIVdiagnosis in your medical record?” If the patient responds “yes”, thenthe system 100 will ask, “Please verify that your diagnosis for HIV waspositive, is this correct?” If the patient responds “yes”, then thesystem 100 writes this fact to the patient's medical history file. Afterconfirmation, each data item is stored in the patient's file in thepatient medical history database 268 (FIG. 6).

At the completion of either updating the history database 268 at state348, state 345 proves to be false, or at the completion of state 347,the system 100 moves to a decision state 349. Before the MDATA system100 ends the consultation with the patient, it presents a summary of allthe advice it has given. The patient is asked to write down and repeatback the key points. The MDATA system 100 then gives the patient theoption of receiving a summary of the consultation session and specificrecommendations provided by the system by either facsimile or firstclass mail. If a fax is desired, the system 100 asks the patient for afax number at state 350. The patient also has the option to send asummary of the consultation to his or her health care provider orspecialist. Proceeding to state 351, the computer 102 adds thetranscript of the current telephone session to a fax queue forsubsequent transmission. At the completion of state 351 or if the system100 determined at state 349 that the session transcript was not to befaxed, the telephone call is terminated at state 352.

VIII. Login Process

Referring now to FIGS. 8 a and 8 b, the patient login process 250defined in FIG. 7 a will be described. This process 250 is called if thepatient has previously called and registered with the system 100.Beginning at a start state 358, the computer 102 moves to state 359 andinitializes a match flag to true. The match flag is checked later inthis process 250 in conjunction with setting the mode of theconsultation. Proceeding to state 360, the computer 102 prompts thepatient for the patient ID (identification) number (PIN) that isassigned during the registration process. The patient registrationprocess 252 will be described in conjunction with FIGS. 9 a and 9 b.Proceeding to a decision state 361, the computer 102 determines whetherthe PIN is valid. If not, the computer 102 determines, at a decisionstate 362, if less than three tries at entering the PIN have beenattempted. If so, the computer 102 loops back to state 360 to repeat therequest for the PIN. However, if three attempts at entering the PIN havebeen made, as determined at state 362, the computer 102 plays a politemessage that advises the patient that the login attempt failed andterminates the call at state 363. The computer 102 reports the failedlogin attempt to the system administrator at the sponsoring agency,hospital or other organization. The patient is allowed to reregister asa new patient, however, to permit access to the needed medicalinformation. The system administrator resolves this type of situationoff-line.

If the patient has correctly entered a valid PIN, as determined at state361, the computer 102 moves to a decision state 364 to determine if thepatient identified by the PIN has a voice print or sample voice waveformon file in the system 100. If not, the computer 102 proceeds to state365 to record the voice print of the patient, e.g., the patient'spronunciation of his or her full name. The patient's voice print may notbe on file, for example, if the patient could not provide a voice printduring the assisted patient registration process 278 in a priorconsultation. At the completion of recording the voice print at state365, the computer 102 advances to state 366 wherein the match flag isset to false to indicate that the patient's voice print was recordedduring the current login.

If the patient identified by the PIN has a voice print on file in thesystem 100, as determined at state 364, the computer 102 proceeds tostate 367 and prompts the patient to pronounce his or her full name.Moving to a decision state 368, the computer 102 determines whether thevoice sample obtained at state 367 passes the matching criteria. If not,the computer proceeds to state 369 and recites a message that thecurrent voice sample does not pass the matching criteria. In thepresently preferred embodiment, the current voice sample is compared tothe reference voice sample recorded during the patient registrationprocess 252 or the assisted patient registration process 278. Becausethe voice samples did not match, as determined at state 368, thecomputer 102 sets the match flag to false at state 370. In this case,the match flag is set to false to indicate that one of the securitychecking methods has failed. However, the process 250 continues at state372 after the match flag is set to false at either state 366 or 370.

If the voice sample passed the matching criteria at state 368, thecomputer 102 advances to state 371 and recites a message that thecurrent voice sample passed the matching criteria. This security checkcondition is now satisfied, and the match flag remains set to true. Atthe completion of state 371 the computer 102 moves to state 372. Atstate 372, the computer 102 verifies the sex and age of the patient byreciting the sex and age, as stored in the enrollment database 260(obtained during the patient registration process 252), to the patient.At a decision state 373, the patient responds to the correctness of therecited information. If the sex or birth date information is notcorrect, the computer 102 moves to state 374 to request the correctinformation. The computer 102 then proceeds back to state 372 to verifythe information received at state 374. If the result of the decisionstate 373 is true, i.e., the sex and age are correct, the computer movesthrough off-page connector A 375 to a decision state 376 on FIG. 8 b todetermine if the patient desires to conduct the telephone session inReal mode or Information mode. If Information mode is desired, thecomputer 102 moves to a decision state 377 to determine if the patient'ssex and age are to be used during the Information mode consultation. Ifnot, the computer 102 moves to state 378 to request an age and sex touse in a hypothetical situation during the Information mode session.Moving to a decision state 379, the computer 102 recites the sex and ageobtained at state 378, and asks the patient to confirm that thisinformation is correct. If not, the computer 102 moves back to state 378to request the age and sex again. When decision state 379 is true or thepatient's age and sex are to be used during this consultation, asdetermined at state 377, the computer 102 moves to state 380 and setsthe operating mode to be Information mode.

If decision state 376 is determined to be Real mode, the computer 102moves to a decision state 381 to check if the match flag is true. Ifnot, the system 100 advises the patient, at state 382, that the currentconsultation is to be performed in Pending mode. The operating mode isset to be Pending mode at state 383. If the match flag is true, asdetermined at state 381, the computer 102 sets the operating mode to beReal mode at state 384.

At the completion of setting the operating mode at either state 380,state 383, or state 384, the computer moves to a decision state 386. Atdecision state 386, the computer 102 determines if the patient desiresto review the touch tone commands described during the registrationprocess. If so, the computer 102 advances to state 388 and recites thetouch tone commands. At the completion of state 388 or if the patientdid not wish to review the touch tone commands, the computer 102proceeds to a decision state 390 wherein the computer 102 determines ifthe patient desires to review the voice keywords described during theregistration process. If so, the computer 102 advances to state 392 andrecites the voice keywords. At the completion of state 392 or if thepatient did not wish to review the voice keywords, the computer 102proceeds to a decision state 394 wherein the computer 102 determines ifthe patient desires to enable prompting. If so, the computer 102advances to state 396 and enables prompting. If not, prompting isdisabled at state 398. To “enable prompting” means that the patientwould like to be prompted for responses. This is referred to as “hard”prompting, since this will remain in effect for the duration of thecall. If hard prompting is off, and the system 100 has difficultyrecognizing patient responses, the computer 102 turns on “soft”prompting. After the next successful recognition, the computer 102 turnsoff soft prompting. At the completion of state 396 or 398, the computer102 returns at state 400 to the top level flow (FIG. 7).

Referring now to FIGS. 12 a and 12 b, the assistant login process 272defined in FIG. 7 b will be described. This process 272 is called if theassistant has previously called and registered with the system 100.Beginning at a start state 940, the computer 102 moves to a state 942and prompts the assistant for the assistant ID (identification) number(AIN) that is assigned during the registration process. The assistantregistration process 274 will be described in conjunction with FIGS. 14a and 14 b. Proceeding to a decision state 944, the computer 102determines whether the AIN is valid. If not, the computer 102determines, at a decision state 946, if less than three tries atentering the AIN have been attempted. If so, the computer 102 loops backto state 942 to repeat the request for the AIN. However, if threeattempts at entering the AIN have been made, as determined at state 946,the computer 102 plays a polite message that advises the assistant thatthe login attempt failed and terminates the call at state 948. Thecomputer 102 reports the failed login attempt to the systemadministrator at the sponsoring agency, hospital or other organization.

If the assistant has correctly entered a valid AIN, as determined atstate 944, the computer 102 proceeds to state 950 and prompts the callerto pronounce his or her full name. Moving to a decision state 951, thecomputer 102 determines whether the voice sample obtained at state 950passes the matching criteria. If not, the computer proceeds to state 952and recites a message that the current voice sample does not pass thematching criteria. In the presently preferred embodiment, the currentvoice sample is compared to the reference voice sample recorded duringthe assistant registration process 274. Because the voice samples didnot match, as determined at state 951, the computer 102 sets theoperating mode to Pending at state 953. In this case, Pending mode isset to indicate that one of the security checking methods has failed.However, the process 272 continues at state 960 on FIG. 12 b afterPending mode is set at state 953.

If the voice sample passed the matching criteria at state 951, thecomputer 102 advances to state 954 and recites a message that thecurrent voice sample passed the matching criteria. This security checkcondition is now satisfied. Next, three additional checks are performedon the assistant identified by the AIN obtained at state 942. At adecision state 955, the computer 102 determines if the permanentassistant flag is true, as stored in the patient and assistantenrollment database 260. If so, the computer 102 advances to a decisionstate 956 to determine if the expiration date for the permanentassistant is in the future, i.e., the expiration date has not beenreached yet. If so, the computer 102 advances to a decision state 957 todetermine if a relationship exists between the assistant and a patient,i.e., the assistant has a relationship pointer to the patient record. Ifso, the operating mode is set to Real at state 958, and then thecomputer 102 advances through off-page connector A 959 to state 960 onFIG. 12 b. However, if any of the decision states 955, 956, or 957 proveto be false, the computer 102 moves to state 953 wherein the operatingmode is set to Pending.

States 960 through 964 are similar to states 372 through 374 of thepatient login process 250 (FIG. 8). Because of this similarity, onlysignificant differences are discussed in the interest of avoidingrepetitiveness. States 960, 962 and 964 verify the assistant's age andsex, rather than the patient as in states 372, 373 and 374. States 966through 980 are similar to states 386 through 400 of the patient loginprocess 250 (FIG. 8 b). The main distinction is that states 966-980pertain to the assistant and states 386-400 pertain to the patient.

Referring now to FIGS. 13 a and 13 b, the assisted patient login process276 defined in FIG. 7 b will be described. This process 276 is called ifboth the patient and the assistant have previously called and registeredwith the system 100. This process allows the patient flexibility bypermitting the assistant to provide help during the login and subsequentconsultation. Beginning at a start state 990, the computer 102 moves astate 992 and prompts the assistant for the patient ID (identification)number (PIN) that is assigned during the registration process. Aspreviously defined in FIG. 7, the assisted patient registration process278 is called if the patient is not already registered. Process 278 willbe described in conjunction with FIGS. 15 a and 15 b. Proceeding to adecision state 994, the computer 102 determines whether the PIN isvalid. If not, the computer 102 determines, at a decision state 996, ifless than three tries at entering the PIN have been attempted. If so,the computer 102 loops back to state 992 to repeat the request for thePIN. However, if three attempts at entering the PIN have been made, asdetermined at state 996, the computer 102 plays a polite message thatadvises the caller that the login attempt failed and terminates the callat state 998. The computer 102 reports the failed login attempt to thesystem administrator at the sponsoring agency, hospital or otherorganization. If the assistant doesn't know the PIN and the patientcannot provide it, the assistant is allowed to reregister the patient asa new patient at process 278 to permit access to the needed medicalinformation. In this case, the assistant may have to estimate the age ofthe patient if the patient has, for example, an altered state ofconsciousness. The system administrator resolves the record-keeping inthis situation off-line.

If the assistant has correctly provided a valid PIN to the system 100 atstate 994, the computer 102 moves to a decision state 993 to determineif the patient identified by the PIN has a voice print or sample voicewaveform on file in the system 100. If not, the computer 102 moves to adecision state 1003 to determine if the patient can provide a voicesample. If not, the computer 102 proceeds through off-page connector B997 to state 1008 on FIG. 13 b. If the patient can provide a voicesample, as determined at state 1003, the computer 102 moves to state 995to record the voice print of the patient, e.g., the patient'spronunciation of his or her full name. The patient's voice print may notbe on file, for example, if the patient could not provide a voice printduring the assisted patient registration process 278 in a priorconsultation. At the completion of recording the voice print at state995, the computer proceeds through off-page connector B 997 to state1008 on FIG. 13 b.

If the patient identified by the PIN has a voice print on file in thesystem 100, as determined at state 993, the computer 102 proceeds tostate 999 and asks whether the patient can provide a voice sample to thesystem. If not, the computer 102 proceeds through off-page connector B997 to state 1008 on FIG. 13 b. States 1000, 1002, 1004, 1006 aresimilar to states 367, 368, 369, 371, respectively, of the patient loginprocess 250 (FIG. 8). Because of this similarity, only significantdifferences are discussed in the interest of avoiding repetitiveness. Atthe completion of state 1004, i.e., the patient's voice sample does notpass the matching criteria, the computer 102 proceeds through off-pageconnector B 997 to state 1008 on FIG. 13 b. At the completion of state1006, i.e., the patient's voice sample does pass the matching criteria,the computer 102 proceeds through off-page connector A 1001 to state1005 on FIG. 13 b.

At the completion of state 995, i.e., the patient's voice print isrecorded, state 999 or state 1003, i.e., the patient cannot provide avoice sample, or state 1004, i.e., the voice sample match fails, thesystem continues process 276 at state 1008 on FIG. 13 b. For the threesituations just described in this process 276, the computer 102 sets theoperating mode to Pending at state 1008. The system 100 then advises thecaller at state 1009 that new patient information will not be saved tothe patient's medical record because the consultation is in Pending modeuntil the information is verified off-line.

At the completion of state 1006, i.e., the voice sample passes, thecomputer 102 continues process 276 at state 1005 wherein the operatingmode is set to Real. The system 100 then advises the caller at state1007 that new patient information will be saved to the patient's medicalrecord.

At the completion of state 1009 or state 1007, the computer 102 moves tostate 1010. States 1010, 1012 and 1014 verify the patient's age and sex,similar to states 372, 373 and 374 (FIG. 8). States 1016 through 1030are similar to states 386 through 400 of the patient login process 250(FIG. 8). The main distinction is that states 1016-1030 are directed tothe assistant and states 386-400 are directed to the patient.

IX. Registration Process

Referring now to FIGS. 9 a and 9 b, the patient registration process 252defined in FIG. 7 a will be described. This process 252 is called if thepatient has not previously called and registered with the system 100.During the first consultation, the MDATA system 100 obtains thepatient's age and sex. This is the minimum amount of information thatthe MDATA system requires in order to give medical advice. The moreinformation the MDATA system has about a patient, however, the morespecific is its advice.

The MDATA system 100 assigns each of its patients a unique patientidentification number. In addition, when a patient initially registers,the patient's own pronunciation of his or her name is recorded,digitized and saved to their medical record. Then, when the patientcalls back, the previous recording is retrieved and the patient is askedto repeat their name exactly as they did during registration. The tworecordings are then compared to see if they match. This use of “voiceprinting” helps to further ensure the security and confidentiality of apatient's medical record.

Beginning at a start state 420, the computer 102 proceeds to state 422,requests the sex of the patient, and verifies by repeating the responsegiven by the patient. Moving to a decision state 424, the patientresponds by indicating to the system 100, via touch tone key or a voiceresponse, whether the repeated information is correct. If not, thecomputer 102 loops back to state 422 to request the information again.When the information is correct at state 424, the computer 102 proceedsto states 426 and 428 to request and verify the birth date of thepatient in a similar fashion to states 422 and 424.

When the decision state 428 is determined to be true, the computer 102proceeds to state 427 and requests the patient to pronounce his or herfull name. Moving to state 429, the full name is digitized and stored ina subdirectory on the hard drive 152 (FIG. 1) indexed by a PatientIdentification Number (PIN). File names are of the form: <PIN>.vox. Thecomputer 102 accesses a file to retrieve the next available PIN. Thepath name to the recorded voice file is saved in the patient's record inthe enrollment database 260. In subsequent telephone sessions with thesystem 100, the patient's voice waveform will be compared to therecorded voice waveform for security and other optional purposes. Whenthe voice waveform is stored, the computer 102 moves to state 431 andprovides the PIN to the patient. The patient is informed of theimportance to save the PIN for use in future consultations with thesystem 100.

At the completion of state 431, the computer 102 moves to a decisionstate 430 to determine if the patient has a MDATA system user pamphletavailable. If so, the computer 102 moves to state 436 and requests thepatient to turn to the pamphlet page that documents the touch tone keys,voice keywords, and modes. If not, the computer 102 moves to a decisionstate 432 to determine if the patient would like the system 100 to pauseto enable the patient to get paper and a writing instrument for writinguser instructions. If so, the computer 102 pauses at state 434 for 30seconds. At the completion of the pause at state 434, if the user didnot desire a pause at state 432, or after the patient is instructed toturn to the proper page of the pamphlet, the computer 102 proceeds tostate 440 of FIG. 9 b via the off-page connector A 438.

At state 440, the system 100 provides an explanation of the touch tonekeys to the patient. These keys were described above in relation to thediscussion on Voice Keywords and DTMF Command Keys. Moving to state 442,the computer 102 asks if the patient desires to hear the explanation ofkeys again. If so, the computer 102 repeats state 440. If not, thecomputer 102 advances to state 444 wherein an explanation of the voicekeywords is provided to the patient. These keywords were previouslydescribed above. Moving to state 446, the computer 102 asks if thepatient desires to hear the explanation of keywords again. If so, thecomputer 102 repeats state 444. If not, the computer 102 advances tostate 448 wherein an explanation of Real and Information modes isprovided to the patient. These modes were previously described above.Moving to state 450, the computer 102 asks if the patient desires tohear the explanation of the modes again. If so, the computer 102 repeatsstate 448. If not, the computer 102 advances to state 452 wherein asummary of new user information is recited to the patient. The summaryincludes a recap of the two methods of controlling the system: voice keywords and DTMF, and the two interaction modes: Real and Info. Thecomputer 102 returns at state 454 to the top level flow (FIG. 7).

Referring now to FIGS. 14 a and 14 b, the assistant registration process274 defined in FIG. 7 b will be described. This process 274 is called ifthe caller is not a registered patient and has not previously called andregistered as an assistant with the system 100. States 1050 through 1090are similar to states 420 through 454 of the patient registrationprocess 252 (FIG. 9). Because of this similarity, only significantdifferences are discussed in the interest of avoiding repetitiveness.States 1052, 1056, 1060, 1062 and 1064 pertain to the assistant ratherthan the patient as in states 422, 426, 427, 429 and 431 (FIG. 9 a),respectively. State 1060 records the assistant's pronunciation of his orher full name and state 1062 saves it in the patient and assistantenrollment database 260. The system 100 provides an assistantidentification number (AIN) at state 1064. The AIN is used similarly tothe PIN in the access of files or records. The remaining states1066-1090 are directed to the assistant also.

Referring now to FIGS. 15 a and 15 b, the assisted patient registrationprocess 278 defined in FIG. 7 b will be described. This process 278 isevoked if the caller is not the patient and the patient has notpreviously called and registered with the system 100. States 1110through 1150 are similar to states 420 through 454 of the patientregistration process 252 (FIG. 9). Because of this similarity, onlysignificant differences are discussed in the interest of avoidingrepetitiveness. The main difference is that the assistant is interactingwith the system 100 on behalf of the patient during this process 278,and therefore, the operating mode is set to Pending at state 1111.States 1112 and 1116 obtain the patient's sex and age, respectively. Ifthe patient cannot provide the age to the assistant and the system, theassistant provides an estimated age. The estimated age can be correctedduring a subsequent consultation with the system 100. At state 1119, thesystem 100 asks whether the patient can provide a voice sample of his orher full name. If so, the voice waveform is recorded and saved inenrollment database 260 (FIG. 6) at states 1120 and 1122. If the patientcannot provide a voice sample at state 1119, the system 100 informs theassistant, at state 1121, that the patient's voice sample will berequested during the subsequent consultation. Then, whether or not avoice sample is recorded, the system 100 provides a patientidentification number (PIN) of the patient to the assistant and thepatient (if coherent) at state 1123. The caller is instructed tosafeguard the PIN for future consultations by either the patient or theassistant on behalf of the patient. If the assistant and/or patientdesires to hear the PIN again, as determined at a decision state 1124,the computer 102 repeats the PIN at state 1123. The computer 102proceeds through off-page connector A 1125 to a decision state 1126 onFIG. 15 b. The remaining states 1126-1150 in process 278 are directed tothe assistant rather than the patient, as in states 430-454 of process252 (FIG. 9).

X. Evaluation Process

Referring now to FIGS. 10 a and 10 b, the evaluation process 254 definedin FIG. 7 d will be described. This process 254 is called if the patienthas selected the Diagnostic System choice in the system selection menu(FIG. 7 d, state 344). Beginning at a start state 470, the computermoves to state 471 and recites a identification method menu to requestcomplaint identification. After the initial screening questions (state306, FIG. 7 a) are completed and a medical record (registration function252) has been opened, the MDATA system 100 asks the patient to describethe complaint. The identification of the patient's problem is one of themost important steps in the evaluation process. The system 100 hasbuilt-in safeguards to ensure that the patient understands the questionsand that the MDATA system 100 understands the patient's complaint. Forexample, the system keeps tables of synonyms so that any problemregarding the semantics of a question or a response can be quicklyresolved. The complaint may be identified in one of four ways: byanatomic system 472, by cause 476, by alphabetic groups 480 or bycatalog number 482.

The easiest and most frequently used way to identify the complaint is byanatomic system, i.e., “what system is your problem in?”. Anatomicsystem 472 refers to basic body systems such as cardiovascular,respiratory, nervous system, digestive, ear/nose/throat, ophthalmology,gynecology/obstetrics, urology, blood/hematology, skin, and endocrine.After the patient has identified the anatomic system of their complaint,they are asked a series of “System Screening Questions” at state 473.For each anatomic system, there are some symptoms or combinations ofsymptoms that, if present, would mandate immediate intervention, suchthat any delay, even to go any further through the menuing process,could cause harm. For example, if the patient has identified thecardiovascular system as the anatomic system in which his or hercomplaint lies (i.e., chest pain), the MDATA system 100 will ask thecardiovascular system screening questions. For example, the patientwould be asked, “Do you have both pressure in your chest and shortnessof breath? If these symptoms are present together, immediateintervention is necessary. With the thrombolytic agents that areavailable today, time is critical in order to save myocardial cells.Just a few minutes can mean the difference between being able toresuscitate a patient or not.

Therefore, at state 474, the system 100 determines if a serious medicalcondition exists. If so, the system 100 moves to state 486, plays amessage that advises the patient to seek immediate medical attention andends the evaluation process 254 at a terminal state 488. If it isdetermined at state 474 that a serious medical condition does not exist,the system 100 proceeds to a complaint menu at state 475 and recites alist of algorithms dealing with the problem that corresponds to theanatomic system selected. The patient then selects an algorithm from thelist.

If the patient is not sure of the anatomic system, the system 100attempts to identify the problem by requesting the cause. Cause 476refers to a cause for an illness or disease such as trauma, infection,allergy/immune, poisoning, environmental, vascular, mental, genetic,endocrine/metabolic, and tumor. Once the patient has identified whatthey think is the cause of their problem (e.g., trauma, infection), theMDATA system 100 asks the “Cause Screening Questions” at state 477.These questions are asked to make sure that the patient is not sufferingfrom an immediate life-threatening problem. For example, if infectionwere chosen as the cause, the system would first rule out thepossibility of epiglottis or meningitis before proceeding. Therefore, atstate 478, the system 100 determines if a serious medical conditionexists. If so, the system 100 moves to state 486, plays a message thatadvises the patient to seek immediate medical attention and ends theevaluation process 254 at a terminal state 488. If it is determined atstate 478 that a serious medical condition does not exist, the system100 proceeds to a complaint menu at state 479 and recites a list ofalgorithms dealing with the problem that corresponds to the causeselected. The patient then selects an algorithm from the list.

Alphabetic groups 480 lists the items in the anatomic system group andthe cause group together in alphabetic order. Moving to state 481, thesystem determines if the selected item is from the cause subgroup of thecombined alphabetic groups. If so, the system 100 proceeds to the “CauseScreening Questions” at state 477. If not, the system moves to the“System Screening Questions” at state 473.

Enter Catalog number state 482 refers to the ability of the patient toselect and enter an individual medical algorithm from a catalog ofmedical algorithms listed in the patient guide distributed to allpatients. At the completion of state 475, 479, or 482, the complaint hasbeen identified, and the computer 102 proceeds to state 483 wherein aseries of “initial” problem screening questions are presented to thepatient. There is a different set of problem screening questions forevery complaint for which advice is offered.

For the purpose of this discussion, “Headache” will be used as anexample to describe how the system approaches the diagnosis of a problemand provides treatment recommendations. As with many problems, there aresome causes of headache that require immediate medical attention. Quiteoften, when a problem is very serious, any delay, even to discuss itfurther, can adversely affect the patient's outcome. The problemscreening questions identify, at a decision state 484, the subset ofpatients whose headaches may require immediate medical care. If aserious medical condition exists, the patient is advised to seekimmediate medical attention at state 486. The computer 102 then ends theevaluation process at state 488 and returns to state 344 in FIG. 7 d.

The following is an example of a problem screening question forheadache:

-   -   ARE YOU CONFUSED, LETHARGIC, OR LESS ORIENTED THAN USUAL?

By asking a question about the patient's level of consciousness, adilemma has been confronted. What does the MDATA system 100 do about thepatient whose problem itself prevents them from appropriately respondingto questions or following advice?

There are some conditions that, by their very nature, may preventpatients from answering questions correctly. For this reason, the MDATAsystem 100 utilizes a “mental status examination” function 508. Themental status examination is a series of questions used to assess thepatient's orientation. This function 508 allows the MDATA system 100 toassess the patent's ability to respond to questions and to followadvice. Although only shown in FIG. 10 b, the mental status examinationfunction 508 is incorporated into the dialogue of any problem whosepresentation could include an altered level of consciousness. Function508 will be further described in conjunction with FIG. 16.

The MDATA system 100 will, of course, be accessed by patients in whom analtered level of consciousness is not expected based on the problem thatthe patient has. The system 100 does anticipate the possibility of thepatient having an altered level of consciousness in some problems, e.g.,when a patient consults the system for striking his or her head, andinvokes the mental status exam function 508. However, an intoxicatedpatient, calling for some other complaint, e.g., a sprained ankle, isone example where the patient may not be able to understand instructionsfrom the system 100. For this reason, the MDATA system also utilizes a“semantic discrepancy evaluator routine” (SDER) function 510. The SDERfunction provides information to the patient and then, after apredetermined period of time, asks the patient to repeat or select theinformation. The patient's answer is then evaluated within system 100.If discrepancies are determined, the system automatically invokes themental status examination function 508. In another embodiment, thesystem 100 asks the patient for some information in different ways atdifferent times, and then compares the patient's responses to determineif they are consistent. If not, the system automatically invokes themental status examination function 508. Although only shown in FIG. 10b, the SDER function 510 is embedded throughout system 100, and israndomly evoked by the computer 102. Function 510 will be furtherdescribed in conjunction with FIG. 17.

Continuing with the headache example at state 483, the MDATA system 100asks the next problem screening question in order to help exclude thepossibility of meningitis, a very serious infection of the centralnervous system.

-   -   IS BENDING YOUR NECK FORWARD SO THAT YOUR CHIN TOUCHES YOUR        CHEST EITHER PAINFUL OR NOT POSSIBLE?        If the answer to this question is “yes”, a serious medical        condition exists at state 484 and the system 100 instructs the        patient to seek immediate medical attention at state 486.

The initial screening questions (state 306, FIG. 7 a) and the problemscreening questions (state 483) can usually be completed within a minuteor so. Once the MDATA system 100 has excluded the causes of headachethat require immediate medical attention, the system becomes a littleless formal and more conversational in the subsequent states. Theexamples given, of course, do not represent all the initial or problemscreening questions.

If no serious medical condition exists, as determined at state 484, thecomputer 102 proceeds to a decision state 490 wherein the system 100identifies those patients who are “re-entering” the system from anearlier consultation. This occurs most frequently when the system 100needs to monitor a patient's symptom over time, or if the system isinitially unable to make a specific diagnosis and would like the patientto re-enter the system again, typically within a few hours. The systemsets an internal re-enter flag to identify the situation where a patientis calling again for the same complaint. If the flag is set at state490, the computer 102 proceeds to state 492 and branches to a re-enterpoint in the evaluation process depending on which medical algorithm hasbeen evoked. The computer 102 moves via off-page connector A 494 tostate 506 (FIG. 10 b) to the appropriate re-enter point.

If the re-enter flag is not set, as determined at state 490, thecomputer 102 moves via off-page connector B 496 to a decision state 499to determine if the consultation is being performed in Real mode orPending mode. If not (i.e., the consultation is in Information mode),the computer proceeds to state 506 to continue the evaluation process.If the consultation is in Real or Pending mode, the computer 102 calls a“meta” function 500 wherein patients are subjected to several “meta”analyses. This concept will be explained in conjunction with FIG. 11,but, in general, it refers to the system's ability to evaluate thepatient's present problem in the context of their past use of thesystem. The Meta function 500 matches various parameters against apredetermined meta threshold. When the MDATA system 100 opens apatient's consultation history file in database 262 (FIG. 6), itcalculates how many times the patient has consulted the system for thesame complaint. For each problem, the MDATA system 100 allows aspecified number of system consultations, per unit of time, before ittakes action. If the meta threshold is reached, as determined at adecision state 502, the MDATA system 100 makes a recommendation based onthis fact alone at state 504. For example, let us assume that thethreshold was set at five headaches in two months. If the patientconsulted the MDATA system 100 for headache more than four times in twomonths, the threshold would be reached and the system would make anappropriate recommendation. The threshold, of course, is different foreach complaint, and may be modified by a set of sensitivity factors thatwill be described hereinbelow. Alternately, the system 100 uses a timedensity ratio (TDR) calculated by the meta function 500 to determine ifa recommendation should be given to the patient.

At the completion of state 504, or if the meta threshold was not reachedat state 502, the computer 102 proceeds to state 506 to continue theevaluation process. State 506 includes a medical algorithm as selectedby the patient in states 475, 479, or 482. Although not necessarily acomplete list, types of medical algorithms include: Headache, Convulsionor Seizure, Chest Pain, Heatstroke, Altered Level of Consciousness,Tremor, Dizziness, Irregular Heartbeat, Fainting, Shortness of Breath,Chest Injury, Depression, Head Injury, Cough, Croup, High BloodPressure, Hyperventilation, Numbness, Wheezing, Inhalation Injury, andStrokes. In addition to meta and past medical history functionality, atleast some of the listed medical algorithms rely upon knowledge of ageand/or sex of the patient as provided in the presently described system100 at time of registration (see FIGS. 9 a and 13 a).

Depending on the medical algorithm and the exact patient condition, oneor more auxiliary functions may be called by state 506 as follows: themental status examination function 508, the SDER function 510, a pastmedical history function 512, a physical self examination function 514,a patient medical condition function 516, and a symptom severityanalysis function 518. These functions will be described hereinbelow.

Returning to the headache example, after the meta analyses (function500) are completed, the MDATA system 100 assesses the severity of thepatient's headache on a one-to-ten scale. The importance of this purelysubjective quantization of the symptom's severity will become apparentlater in this description.

Although the MDATA system's paradigm is fundamentally an algorithmicone, the underlying logic of the diagnostic process for headache will bedescribed. The MDATA system 100 begins the diagnostic process forheadache by referring to three lists stored internally in the computer102.

The first list is a ranking of the most common causes of headache in thegeneral population. The most common cause is ranked first, the secondmost common is ranked second, and so on. In other words, the first listranks all the causes of headache in the general population in decreasingfrequency of occurrence.

The second list is a ranking of the various causes of headache accordingto the seriousness of the underlying cause. The more serious causes arepositioned toward the top of the list, the less serious toward thebottom. For example, meningitis, brain tumor, and subarachnoidhemorrhage would be the top three causes on the second list.

The third list is quite similar to the second list. It ranks the causesof headache according to the rapidity with which intervention isnecessary. The causes of headache that require immediate intervention,such as meningitis and subarachnoid hemorrhage, are toward the top. Theproblem screening questions (state 483) were developed from this list.

During the evaluation process 254, the MDATA system 100 asks the patienta series of “diagnostic screening questions.” From the answers to thesequestions, along with any physical signs elicited from the patient (fromfunction 514), under the direction of the MDATA system 100, the systemestablishes the most likely cause of the patient's headache.

The following are examples of diagnostic screening questions forheadache:

-   -   DO YOU EXPERIENCE MORE THAN ONE KIND OF HEADACHE?    -   DO YOU, OR DOES ANYONE ELSE, KNOW THAT YOU ARE GOING TO GET A        HEADACHE BEFORE THE ACTUAL PAIN BEGINS?    -   DO YOUR HEADACHES FREQUENTLY WAKE YOU UP AT NIGHT?    -   DO YOUR HEADACHES USUALLY BEGIN SUDDENLY?

Based upon the answers to the diagnostic screening questions, the MDATAsystem 100 reorders the first list. The first list then becomes a listof the possible causes of headache in decreasing levels of probabilityin the patient seeking consultation. The first list is now patientspecific. If the MDATA system 100 concludes that migraine is the mostlikely cause of the patient's headache, then migraine will now be rankedat the top of the first list.

The MDATA system 100 is knowledgeable about the difference betweenclassic, common, and all other variants of migraine, but for thisdiscussion the general term “migraine” will be used. After reorderingthe first list and placing migraine at the top, the MDATA system 100then asks several questions related specifically to migraine headaches.These are called the “migraine screening questions.” The probabilitythat the patient actually has a migraine headache is calculated from theanswers to these questions. Each cause of headache has its own set ofscreening questions, physical examination signs, and, if the patient hasthe MDATA system's Home Diagnostic and Treatment Kit, appropriatelaboratory tests.

The following are examples of migraine screening questions:

-   -   IS EITHER NAUSEA OR VOMITING ASSOCIATED WITH YOUR HEADACHE?    -   ARE VISUAL DISTURBANCES ASSOCIATED WITH YOUR HEADACHE?

After obtaining the answers to the migraine screening questions, if theprobability that the patient is suffering from a migraine headache doesnot reach an established threshold, the next cause of headache on thereordered first list is considered and pursued as a diagnosis.

If the probability of having a migraine headache does reach thethreshold, the MDATA system 100 asks the patient several more questionsdesigned to confirm the presence of migraine, given the fact that thesystem has already determined that it is the most likely diagnosis.These are called the “migraine confirmation questions.” Just as eachcause of headache has a set of screening questions, each cause ofheadache also has a set of confirmation questions.

The following are examples of migraine confirmation questions:

-   -   DOES ANYONE WHO IS RELATED TO YOU BY BLOOD HAVE MIGRAINE        HEADACHES?    -   WHEN YOU HAVE A HEADACHE DO YOU FEEL MORE LIKE LYING DOWN OR        WALKING AROUND?

From the answers to the migraine confirmation questions, the MDATAsystem 100 calculates the probability of confirmation of migraine. InBayes' terms (which refer to the probability of certainty of adiagnosis) this is called a “conditional probability.”

If the probability of migraine headaches reaches threshold, but theprobability of confirmation of migraine does not reach threshold, then,as mentioned, the system pursues the next diagnostic cause of headacheon the patient specific list.

If the probability of the second cause of headache (say cluster) reachesthreshold, then the “cluster confirmation questions” are asked. If theyreach threshold, then again the serious causes of headache are excludedas a diagnosis.

The MDATA system 100 stores the scores of all the screening andconfirmation questions in what are called “session memory variables”that are installed in the symbol table. It is, in part, these scoresthat are then used to determine the probability of one diagnosis versusanother.

For example, if the answers to the cluster confirmation questions do notreach threshold, then the scores of the screening and confirmationquestions of migraine and cluster are compared to see which cause is themore probable.

Whichever has the higher score, or exceeds the other by a predeterminedthreshold, is then assumed to be the more probable cause. The list is,if necessary, again reordered. This time it becomes the final diagnosticlist which is a list of differential diagnoses in decreasing levels ofprobability for this patient.

All of the headache scoring thresholds are modified or modulated by aseries of sensitivity factors as are all aspects of the system in whichscalar thresholds are used. The sensitivity factors are discussedhereinbelow in section XVIII. For example, if it was found that a subsetof patients in which the diagnosis of meningitis was not being made asearly as it should be, then the sensitivity factor modifying thetemperature threshold could be decreased so that now, a patient with alower temperature would be instructed to seek an immediate evaluation.

Before discussing the results with the patient, however, the MDATAsystem 100 must again rule out the serious causes of headache. Theproblem screening questions have already filtered out those patients whohave a serious cause of headache, such as meningitis, that requiresimmediate medical intervention.

The MDATA system 100 now proceeds to eliminate those causes of headachethat, although serious, do not require immediate medical attention. Forexample, although a brain tumor is a serious cause of headache, it isnot as immediately life threatening as meningitis or subarachnoidhemorrhage. To accomplish this task, the MDATA system 100 sequentiallyanalyzes the serious causes of headache that are located at the top ofthe second list. The MDATA system 100 again asks the patient the set ofscreening questions associated with each of the serious causes ofheadache. This time, however, the MDATA system 100 makes sure that theprobability of having any of the serious causes of headache issufficiently low in order to exclude them from diagnostic consideration.Only after this is accomplished will the system discuss its conclusionand recommendations with the patient.

The discussion that the MDATA system would have with the migraineheadache patient would include the following:

-   -   Its diagnostic impression, or its diagnostic impressions in        decreasing levels of probability.    -   Its estimate of the level of probability of migraine.    -   Whether or not the system feels it has excluded the serious        causes of headache to a level of certainty that satisfies the        system.    -   What tests, if any, should be obtained to confirm or exclude a        diagnosis.    -   How soon to see a physician.    -   What kind of physician to see (e.g., family practitioner,        internist, or neurologist).    -   What kind of information to bring to the physician when a        consultation is obtained.    -   Questions to ask the physician.    -   The latest treatment for migraine.

Even if the MDATA system 100 cannot determine with sufficient certaintywhat is causing the headache, it can still provide patients withvaluable information and advice. For example, the patient may be toldthe following:

“At this time, the MDATA system is unable to pinpoint a particular causeof your headache with the degree of certainty required to make specificrecommendations. The MDATA system, however, suggests a consultation witha neurologist. You can either call your family practitioner or internistand ask for a referral.

“While you are waiting to be seen by the neurologist, there are manythings that you can do in order to help the physician diagnose yourheadache. Many headache experts have found that a record of when theirpatients' headaches occur and how bad they are is very helpful infinding both the cause of the headache as well as the best treatment.

“In order to assist you, the MDATA system will send you a blank calendaron which you can record the time and severity of your headaches. Inaddition, there is space for you to record what seems to bring on theheadaches, makes them worse, or makes them better. The MDATA system willalso send you a questionnaire to fill out and give to your doctor,containing a list of questions asked by some of the world's leadingheadache experts when they are trying to arrive at a diagnosis.”

A full set of instructions will be provided.

The MDATA system is able to customize the information given to patientsto accommodate the individual needs of a sponsoring agency or group suchas a Health Maintenance Organization (HMO) or a Managed Care Plan. Forexample, if the system finds that the patient should see a physician,the MDATA system can determine from a patient's medical record whetherthey have an established relationship with an appropriate specialist. Ifthey do, the specialist's name and phone number, or a list ofparticipating specialists for their HMO or Managed Care Plan and anyspecific instructions, will be given to the patient with therecommendation to make an appointment within a specific time frame.

At the conclusion of state 506, the system may or may not have areasonably certain diagnosis available. For example, the headachealgorithm provides a diagnosis of migraine in response to a particularset of patient symptoms. In situations where the MDATA system 100 cannotdetermine with sufficient certainty what is causing a particular problem(no diagnosis) or in a situation where a diagnosis is available butadditional information is desirable, e.g., to determine a trend, are-enter flag may be set by the system 100. At a decision state 520, thecomputer 102 determines if re-enter criteria are met for the currentalgorithm and patient situation. If so, the computer sets the re-enterflag at state 522 for this problem so a subsequent telephoneconsultation by the patient will allow for additional information to beadded to the patient record by the system in full knowledge of theprevious call. This additional information may yield a better diagnosis.

If the re-enter criteria were not met, as determined at state 520, thecomputer 102 proceeds to a decision state 524 to determine if thepatient desires to hear treatment information for the current problem.If so, the computer 102 calls the treatment table process 256, whichwill be described in conjunction with FIGS. 22 and 23. If the patientdoes not wish to hear treatment information at this time, the computer102 advances to a decision state 528 to determine if the patient wouldlike to investigate another complaint through the evaluation process254. If so, the computer 102 moves via off-page connector C 530 to state471 on FIG. 10 a to repeat the process 254. However, if the patient doesnot wish to pursue another complaint, the computer returns at state 532to the top level flow (FIG. 7 d).

XI. The Meta Function

Referring now to FIGS. 11 a and 11 b, the meta function 500 defined inFIG. 10 b will be described. One of the many ways the MDATA system 100is qualitatively different from prior ways of providing medical adviceis in its use of the “meta function.” As mentioned earlier, “meta”refers to the system's ability to evaluate the patient's present problemin the context of his or her past use of the system. The meta functionallows the system 100 to make an inference based upon the number andfrequency of previous patient consultations (or, “medical complaints”)and the system's previous diagnostic assessments. Every patient who haspreviously used the MDATA system 100 undergoes the meta analysis.

Input Parameters

The meta function 500 has five input parameters listed at state 540 asfollows:

-   -   i. Problem String (PS)—a four character alphanumeric string        indicating the patient's complaint. The first character of this        string is taken from the Systems column of Table 2. For example,        ‘N’ denotes the nervous system. The second through fourth        characters identify an individual complaint, e.g., “NHDA”        identifies headache. Other examples:

CCHP Chest Pain NINJ Head injury RINH Inhalation of toxic fumes

-   -   ii. System String (SS)—a four character alphanumeric string that        indicates the affected anatomic system. The first character is        taken from the Systems column of Table 2. The second through        fourth characters are encoded with subsystem identification, or        filled with the ‘*’ wildcard character. For example, “N***” will        match all cases that involve the nervous system.    -   iii. Cause String (CS)—a ten character alphanumeric string that        indicates the cause of the patient's complaint. The first        character is taken from the Causes column of Table 2. The second        through tenth characters are filled in as needed to more closely        specify the cause of interest. A very broad example is        “I*********” which denotes any infection. Other examples which        illustrate how the cause string can become very specific:        -   IV******** Viral infection        -   IB******** Bacterial infection        -   IBN******* Gram negative bacterial infection        -   IBNM****** Meningococcal gram negative bacterial infection    -   iv. Beginning Time (T₁)—a timestamp value which indicates the        date and time to be used for the beginning of the time window        under consideration.    -   v. Ending Time (T₂)—a timestamp value which indicates the date        and time to be used for the end of the time window.

Table 2 lists code letters used as the first letter of the meta stringparameter:

TABLE 2 Causes Systems A - allergy B - bones/orthopedics E - environmentC - cardiology I - infection D - gastro-intestinal M - mental G -gynecology P - poison H - hematology (blood) T - trauma L - larynx (ENT)V - vascular N - nervous X - genetic (chromosomal) O - opthamology Y -nutritional/metabolic/endocrine R - respiratory Z - tumor (cancer) S -skin U - urology

A set of meta function analyses involves the identification of trends inthe patient's medical history. For example, if a patient went to his orher doctor with a history of gradually worsening headaches (either morepainful, more frequent, or both) the physician would consider thisworsening trend in his or her management of the case. The MDATA system100 also does this.

Meta Analysis

The algorithm author passes input parameters to the meta function byusing the keyword Meta, followed by the input parameters enclosed inparentheses. The format for the meta function is:Meta(PS,SS,CS,T₁,T₂)

Two types of analysis are performed by the meta function:

-   -   i. Pattern Matching    -   ii. Time density

i. Pattern Matching

In pattern matching analysis, the meta function compares the inputstrings with the record fields in the patient's consultation historydatabase 262. The use of the ‘*’ wildcard character in the input stringwill cause the meta function to ignore the corresponding characterposition in the record field, thereby enabling the meta function toexamine only the fields of interest. By providing input strings that areeither general or specific, the fields of interest for analysis areselected. For example,Meta(“NHDA”,“****”,“**********”,T₁,T₂)will cause the meta function to only consider past consultations for theproblem of headache, regardless of the anatomic system and causeinvolved.

Through the use of a common syntax, the meta process supports four typesor modes of pattern matching analysis, shown here through examples:

(a) Problem Analysis:Meta(“NHDA”,“****”,“**********”, 06/01/93, 12/31/93)

Here the meta function will find the number of complaints of headachesthat occurred between Jun. 1, 1993 and Dec. 31, 1993.

(b) Anatomic System Analysis:Meta(“****”,“D***”,“***********”, 06/01/93, 12/31/93)

Here the meta function will find the number of complaints involving thegastro-intestinal system between Jun. 1, 1993 and Dec. 31, 1993. Forexample, if a patient consulted the MDATA system 100 once for abdominalpain, once for vomiting, and once for diarrhea, but each on a differentoccasion, the system would recognize that these are all problemsinvolving the gastrointestinal tract.

(c) Cause Analysis:Meta(“****”,“****”,“IB*********”, 06/01/93, 12/31/93)

Here the meta function will find the number of complaints that werefound to be caused by bacterial infection between Jun. 1, 1993 and Dec.31, 1993. The problems (complaints) caused by bacterial infection couldbe in different parts of the body.

(d) Combination Analysis:Meta(“NHDA”,“****”,“I**********”, 06/01/93, 12/31/93)

Here the meta function will find the number of complaints of headachethat were found to be caused by infection between Jun. 1, 1993 and Dec.31, 1993.

ii. Time Density

If the pattern matching analysis finds at least three matching recordsin the patient's consultation history database 262, then the metafunction performs a time density analysis. Time density refers to theamount of time between each consultation. If the amount of time betweenconsultations is getting shorter, then the frequency of consultationsuggests that the nature of the complaint is getting worse. Time densityanalysis reveals when a problem is getting better, and when it isgetting worse.

Time density analysis uses the meta records that matched the patternmatching criteria. The computer designates the most recent meta record‘n’, the next most recent is record ‘n−1’, and the second most recent isrecord ‘n−2’. The time stamp of each meta record is examined, and twotime difference values, X and Y, are determined according to theformula:X=time difference(n−2,n−1)Y=time difference(n−1,n)

The ratio of these time differences produces the time density ratio(TDR):Time Density Ratio=X/Y

The significance of the time density ratio value can be seen through thefollowing examples:

Example 1 Time Between Consultations is the Same

Consultation Date of Consultation n − 2 Jun. 01, 1993 n − 1 Jun. 08,1993 n Jun. 15, 1993Calculate:X=time difference(06/01/93, 06/08/93)=7 daysY=time difference(06/29/93, 06/15/93)=7 daysTime Density Ratio=7 days/7 days=1.0

Example 2 Time Between Consultations is Getting Shorter

Consultation Date of Consultation n − 2 Jun. 01, 1993 n − 1 Jun. 22,1993 n Jun. 29, 1993Calculate:X=time difference(06/01/93, 06/22/93)=21 daysY=time difference(06/29/93, 06/22/93)=7 daysTime Density Ratio=21 days/7 days=3.0When consultations are occurring at even intervals, then the TDR valueis close to unity. If the frequency of consultations is decreasing, thenthe TDR value will be less than 1.0. This would be typical of a problemthat is resolving itself. If the frequency of consultations increases,then the TDR value will be greater than one. In the second example, theTDR value of 3.0 indicates a consultation rate increase of three timesduring the analysis period. This would be typical of a problem that israpidly getting worse.Return ValuesAfter the meta function returns, two local memory variables areinstalled in the symbol table and contain the results of the metaanalysis:

-   -   i. Match Counter (MC)—an integer that contains the number of        meta string matches found within the time window.    -   ii. Time Density Ratio (TDR)—a floating point value that        expresses whether the frequency of meta string matches is        increasing or decreasing.        After calling the meta function, the algorithm author can then        make decisions based upon the values returned in these two        memory variables.        For example:        Meta(“NHDA”,“****”,“**********”, 06/01/93, 12/31/93)

If MC>=3 then 100 else 101

The meta function counts the number of complaints of headache betweenJun. 1, 1993 and Dec. 31, 1993. If the number of complaints found (MC)is greater than or equal to 3, then the evaluation process branches tonode 100; otherwise it branches to node 101.

Another example:Meta(“****”,“****”,“I*********”, 06/01/93, 12/31/93)

If TDR>=2.0 then 200 else 201

The meta function is invoked to count the number of diagnoses attributedto a cause of infection. If the infection caused diagnoses found have atime density ratio greater than or equal to 2.0, then the evaluationprocess branches to node 200; otherwise it branches to node 201.

Referring again to FIG. 11 a, the meta function 500 initializes at state540 by popping the input parameters off the run-time stack and storingthem in local memory variables: PS for problem string, SS for anatomicsystem string, CS for cause string, T₁ for the beginning date and T₂ forthe ending date. After the start state 542, the computer moves to state544 and initializes the pattern match counter to zero.

The computer 102 then moves to state 546 wherein it begins the patternmatching analysis. The computer 102 reads the first meta record in thepatient's consultation history database 262 and moves to a decisionstate 548 wherein it examines the record's timestamp. If the timestampfalls within the time window established by the input parameters T₁ andT₂, then the computer will move to state 550; otherwise it moves tostate 554. At state 550, the computer 102 compares the contents of themeta record problem field with the input string PS, the meta recordanatomic system field with the input string SS and the meta record causefield with the input string CS. If all these fields match the respectiveinput strings, then the computer moves to state 552 wherein the matchcounter MC is incremented, and then the computer moves to state 554. Ifthere is any mismatch between a meta record field and its respectiveinput string, then the computer moves to state 554 and does notincrement MC.

At decision state 554, the computer 102 determines if there are moremeta records to process. If so, the computer 102 moves to state 556wherein it reads the next record and then moves back to state 548 toperform the time window determination. The meta function iteratesthrough this pattern matching until all of the meta records have beenread. When there are no more meta records to be processed, the computermoves through off-page connector A 558 to a decision state 560 on FIG.11 b wherein a determination is made if the value of the match counterMC is greater than or equal to 3. If so, then the computer moves tostate 564 wherein it begins the time density analysis.

At state 564, the computer 102 locates the three most recent metarecords whose fields matched the input strings. The computer designatesthe most recent meta record ‘n’, the next most recent is record “n−1”and the second most recent is record ‘n−2’. The computer then moves tostate 566 wherein it calculates X, the time difference between thetimestamps of records n−2 and n−1, and Y, the time difference betweenrecords n−1 and n. The computer 102 then moves to state 568 wherein itcalculates the time density ratio (TDR) as the time X divided by time Y.

If the computer 102 determined at state 560 that there were less thanthree matches, then it would move to state 562 wherein it sets the valueof the time density ratio (TDR) to 0.0, which indicates that the timedensity analysis could not be performed. At the completion ofestablishing the value of TDR at either state 562 or 568, the computer102 moves to terminal state 570 wherein the meta process terminates,returns the match counter MC and the time density ratio TDR, and returnscontrol to the evaluation process 254 (FIG. 10).

The interaction of the meta analyses for cause and for anatomic systemcan be conceptualized by means of a simple geometric metaphor. Considera two dimensional array in which the causes of disease (trauma,infection, allergy/immune, and so forth) are placed on the “Y” axis, orordinate, and the anatomic systems of the body (cardiac, respiratory,nervous system, and so forth) are placed on the “X” axis or abscissa.Disease then can be represented by, or is produced at, the intersectionof the lines drawn from the applicable cause and the anatomic system.

As a very simple illustration, consider the two-dimensional array shownin Table 3 (FIG. 24). The array of Table 3 shows an infection in thecentral nervous system represented at the intersection of the cause ofdisease (infection) and the anatomic system involved (the nervoussystem).

Of course, each cause of disease can be further divided into subcauses.For example, infection would be broken down (or subdivided) intobacterial and viral, and bacterial would be further broken down intogram positive and gram negative, and gram positive would be further yetbroken down into streptococcus, and so on. The anatomic systems could bebroken down in a similar way.

As a patient uses the system 100, and as the meta analyses for cause andfor anatomic system attribute causes to disease processes and record theanatomic systems involved, a three-dimensional cube (a “meta cube”) isproduced composed of these stacked two-dimensional arrays. The “Z” axiscoordinate of each layer is the time of the patient's consultationobtained from the system clock (i.e., the moment that the actualintersection of the cause and anatomic system occurs indicating thediagnosis).

The “meta cube” then represents a summation of the patient's interactionwith the system 100 through time. Although much of the patient's pasthistory is stored using ICD-9-CM codes as well as conventional textstrings in fields of the patient's medical record, the “meta cube”technique allows very useful analyses to be done.

Using the same modeling metaphor, the “Z” axis coordinate can be used torepresent the practice of medicine. Here the “Z” coordinate is againtime, but in this representation, time refers to a spectrum of ages frompediatrics to geriatrics. Thus, each coronal plane representsspecialties by time, e.g., pediatrics, adolescent medicine, adult,geriatric. A vertical plane describes a specialty by anatomic site, suchas neurology or cardiology, while a horizontal plane describes aspecialty which practice is bounded (subsumed) by (on) cause, such asoncology or infectious disease. To further this metaphor, the rapiditywith which intervention is necessary could be a fourth dimension of themodel, and the frequency of an occurrence of a disease is the fifthdimension. Ethical and moral responsibility could be a sixth dimensionof the model.

Node Map Traverse Analysis

The MDATA system 100 uses a “neural net emulator” program to determineif patterns produced by patients, as they traverse down the nodes(creating “node tracks” of the algorithms in the course of aconsultation, may be early predictors of disease. Somewhat like the“meta cube,” the “node tracks” can be superimposed, rather than stacked,upon one another to create a two-dimensional array. This time, however,the pattern produced represents the sum of the patient's previousconsultations. In the MDATA system 100, this is called a “node tracktraverse analysis.”

For example, the MDATA system 100 may discover that the pattern that isproduced when a patient consults the system, at different times, forepisodes of diarrhea, cough, and oral candidiasis may be predictive ofAIDS. Or, that the pattern produced when a patient consults the systemfor increased frequency of urination and weight loss may be predictiveof diabetes mellitus.

XII. Mental Status Examination

Referring to FIGS. 16 a and 16 b, the mental status examination function508 defined in FIG. 10 b will be described. The mental statusexamination is a series of questions used to assess the patient'sorientation that allows the system 100 to determine the patient'sability to respond to questions and to follow advice. The examination isautomatically incorporated into the dialogue of any problem whosepresentation could include an altered level of consciousness. If anoperator or nurse monitoring a telephone consultation at any time feelsthere may be a problem with the caller's ability to understand orrespond to questions, the mental status examination may also be manuallyinvoked.

If the MDATA system 100 determines that the patient is not sufficientlyoriented based on the results of the mental status examination, thesystem 100 will ask to speak to someone other than the patient. If noone else is available, the MDATA system 100 can contact the emergencymedical services system in the patient's area if the system knows thepatient's present geographic position.

Beginning at a start state 680 of FIG. 16 a, the computer 102initializes the value of a variable Score to be zero. Moving to state682, the computer asks the patient Question #1. In the presentlypreferred embodiment, the question is “what day of the week is it?” Ifthe person answers the question correctly, as determined by a decisionstate 684, the computer 102 increments the value of Score by one. AfterScore is incremented or if the patient did not answer the first questioncorrectly, the computer 102 moves to state 688 wherein the computer 102asks the patient Question #2. In the presently preferred embodiment, thequestion is “what month of the year is it?” If the person answers thequestion correctly, as determined by a decision state 690, the computer102 increments the value of Score by one. After Score is incremented orif the patient did not answer the second question correctly, thecomputer 102 moves to state 694 wherein the computer 102 asks thepatient Question #3. In the presently preferred embodiment, the questionis “who is the President of the United States?” If the person answersthe question correctly, as determined by a decision state 696, thecomputer increments the value of Score by one. After Score isincremented at state 698 or if the patient did not answer the thirdquestion correctly, the computer 102 moves through off-page connector A699 to a decision state 700 on FIG. 16 b.

At decision state 700, the computer 102 compares the score to the mentalstatus exam threshold at a decision state 700. If the score meets orexceeds the threshold, then the mental status exam returns to theevaluation process at state 701 and the diagnostic evaluation continues.If the score does not reach or exceed the threshold value, the computer102 moves to state 702 wherein the operating mode flag is set toPending. The MDATA system 100 will then ask, at a decision state 703, ifsomeone else is available to continue the consultation. If no one elseis available, any new information gathered up to this point in thesession is saved to Pending file 269 at state 704 and then, at state705, the telephone call with the patient is transferred to a medicalstaff person. If someone else is available, as determined at state 703,and is able and willing to continue the evaluation process of thepatient, as determined at state 706, the computer 102 asks the person ifhe or she is a registered assistant at state 707. If the person responds“yes”, the computer 102 invokes the assistant login process 272 at startstate 940 on FIG. 12 a. If the person is not a registered assistant, thecomputer 102 invokes the assistant registration process 274 at startstate 1050 on FIG. 14 a. After assistant registration or login, thecomputer 102 moves to terminal state 708 wherein the mental statusexamination process terminates and the evaluation process 254 resumes.At the end of the evaluation process 254, any new information gatheredduring the session will be written to the patient's past medical historyfile at state 348 or to pending file 269 at state 347 on FIG. 7 d,depending on whether the session continued in real or pending mode. Inthe presently preferred embodiment, the value of Score could be zero,one, two, or three. Of course, in other embodiments, different questionsto be asked of the patient may be utilized in the mental status examfunction 508.

XIII. Semantic Discrepancy Evaluator Routine

Referring to FIG. 17, the semantic discrepancy evaluator routine (SDER)510 defined in FIG. 10 b will be described. The SDER 510 uses one ormore questions that ask for the same information at different times, andin other embodiments, in different ways. The answers given by thepatient are then compared within the system 100 to help determine themental status of the patient.

Beginning at a start state 712, the computer 102 moves to state 716 andrecites a message to the patient. In the presently preferred embodiment,the message is “remember this three digit number. NUMBER”, where thecomputer generates a random three digit number (i.e., in the range 100to 999 inclusive) as NUMBER which is kept in a session memory variable.

Then, after a predetermined time interval at state 718, the computer 102moves to state 720 and recites a request of the patient. In thepresently preferred embodiment, the request is “please tell me the threedigit number.” The computer 102 then compares the number given by thepatient in response to state 720 against the NUMBER kept in the memoryvariable at a decision state 722. If the numbers match, the computer 102returns at state 724 with a status of pass to the evaluation process(FIG. 10 b). If the numbers do not match, the computer 102 returns atstate 726 with a status of fail to the evaluation process. In thepresently preferred embodiment, if the return status of the SDER 510 is“fail”, the evaluation process 254 automatically invokes the mentalstatus examination function 508.

XIV. Past Medical History Routine

Referring to FIG. 18, the Past Medical History Routine (PMHR) 512defined in FIG. 10 b will be described. The contents of a patient's pastmedical history file, which is part of the PMH database 268, are loadedto the symbol table when the patient logs in to the system 100. Duringthe evaluation process 254, a TEST is performed by the computer 102before a particular medical algorithm is initiated to verify thatnecessary items are present in the symbol table. The effect of anegative TEST result is that the system 100 prompts the patient toprovide the missing past medical history information via the PMHR 512.

The PMHR 512 uses an input parameter “condition label” (L) as indicatedat State 740. The “Condition label” is unique, e.g., PMHRLTB1corresponds to the first PMH object tested in the croup (RLTB)algorithm: diagnosis for croup in children. The label is passed so thatPMHR 512 knows what questions to ask. The ability of the system 100 toask a past medical history question in the middle of the evaluationprocess 254 is a feature that saves the patient from having to answerthe entire PMH questionnaire during the registration process. TheBoolean result, or scalar value, is stored in the symbol table underthis label (PMHRLTB1), and the algorithm can use it in decision making,e.g., If PMHRLTB1=True Then 4310 Else 4320.

Beginning at a start state 742, the computer 102 moves to state 744 andprompts the patient for the missing medical condition data. Moving tostate 746, the computer 102 repeats the information provided at state744 and asks the patient if the repeated information is correct. Movingto a decision state 748, the patient responds by indicating whether therepeated information is correct. If the data is not correct, thecomputer 102 proceeds to state 750 to determine if the patient wouldlike to attempt the data entry step again. If so, the computer 102 loopsback to state 744 and prompts the patient for the data again. If not,the computer 102 returns at state 754 to the evaluation process (FIG. 10b).

If the newly-entered data is correct, as determined at state 748, thecomputer 102 advances to state 752 and installs the condition label (L)and the data value in the symbol table for the patient. The computer 102then returns at state 754 to the evaluation process 254.

XV. Physical Self Examination

Referring to FIG. 19, the Physical Self Examination function 514 definedin FIG. 10 b will be described. A physical examination can actually bedone by the patient under the direction of the MDATA system 100. TheMDATA system 100 is designed to function primarily based upon responsesto carefully crafted questions, i.e., history, and physical findingselicited from the patient. There are times, however, when the additionof certain laboratory tests can increase the accuracy of the diagnosisas well as help determine the appropriate treatment recommendations. Forthis reason, a MDATA system Home Diagnostic and Treatment Kit isavailable for use by patients. If the patient has the Home Diagnosticand Treatment Kit, including visual field cards, Snelling chart, andpossibly the MDATA system's “tele-stethoscope” to assess intracranial orcarotid bruits, this information will be used in the diagnostic processas well.

The MDATA system 100 is also able to play tones of different frequenciesand intensities to emulate audiometric testing for hearing acuity. Thisallows, for example, the MDATA system 100 to detect the unilateraldecrease in hearing caused by an acoustic neuroma.

Beginning at a start state 770, the computer 102 branches to one or morephysical self examination procedures depending on the current problemand what equipment if any is available for use by the patient. Theseprocedures include: home diagnostic tests 772, vital signs 774,observable physical signs 776, clinical sound recording 778, andtele-stethoscope 780.

A variety of home diagnostic tests 772 are available for use by thepatient. New advances in biotechnology, including a new generation ofurine dipsticks such as a “Multistix 8 SG” produced by Ames andmonoclonal antibody tests such as “ICON® STREP B” produced byHybritech®, allow an entire spectrum of laboratory tests to be performedat home by the patient under the direction of the MDATA system. Forexample, urine dipsticks can be used to check for blood, nitrites,leukocytes, or leukocyte esterase indicating cystitis or a bladderinfection.

In order to use much of the monoclonal antibody technology, however, asmall amount of blood must be obtained by using a fingertip lancet. Thisis already successfully being done by diabetics at home who use aglucometer to measure their blood sugar after pricking their finger toget a small sample of blood.

The MDATA system Home Diagnostic and Treatment Kit also containsequipment to allow the patient, or someone else, to measure thepatient's vital signs 774. A blood pressure cuff and thermometer areincluded with instructions for their use as well as instructions tomeasure pulse and respiratory rate.

The patient may be directed by the system 100 to observe variousphysical signs 776. For example, a headache patient will be asked topalpate their temporal artery area, and to look at themselves in themirror to identify the ptosis and tearing of a cluster headache or toidentify the steamy cornea that may occur with acute narrow angleglaucoma.

As an example of how the MDATA system Home diagnostic and Treatment Kitcould be helpful, consider a woman who (using the MDATA system's urinepregnancy test based on ICON® II HCG ImmunoConcentration™ Assay,produced by Hybritech®) finds out that she is pregnant. This is herfirst pregnancy. Later, when consulting the system for headache, a urinedipstick indicates protein in her urine and the measurement of her vitalsigns shows a significant rise in her blood pressure. This is a classicpresentation of preeclampsia.

Instead of going to a doctor's office, patients could also use the MDATAsystem's Home Diagnostic and Treatment Kit to collect samples at homeand then send them to a designated lab for analysis as needed. Thissaves time for the patient and is especially useful if the patient hasdifficulty in traveling. Costs should also be minimized in this type oflaboratory analysis.

The MDATA system 100 records clinically relevant sounds 778 of a patientsuch as the cough of bronchitis, the seal bark cough of croup or theinspiratory stridor of epiglottis. These sounds are digitized and storedin the patient's medical record. Then, using the re-enter feature of thesystem 100, the system can monitor, for example, a patient's cough overtime to be sure that the cough is resolving as it should.

The general concept of recording and analyzing a cough is disclosed inthe article A microcomputer-based interactive cough sound analysissystem, C. William Thorpe, et al., published in Computer Methods andPrograms in Biomedicine, 1991. The cough sound analysis system describesthe filtering, amplification, recording, and software processing of acough sound. The MDATA system 100 uses the telephone handset microphonein conjunction with an amplifier to procure the clinical sounds. Thesesounds are then transmitted to the system 100 where they are filtered,digitized using VP board 122 and recorded to a file in the patientmedical history database 268 on the hard drive 152 (FIG. 1).

The MDATA system 100 is building a library of clinical sounds thatallows patterns or profiles to be developed that relate the wave form ofthe clinical sound to the probability of a particular diagnosis. Forexample, the MDATA system 100 could compare the cough of a patient tothe sound library to see if the cough of the patient is similar to thosethat eventually have been diagnosed as lung cancer.

In addition, the patient's record of the pronunciation of his or hername may be periodically recorded and compared to previous recordings.This allows the MDATA system 100 to potentially detect and evaluate thehoarseness that could be produced by a nodule on the patient's vocalcords.

A “tele-stethoscope” 780 is a device that allows the sounds a physicianwould hear through a stethoscope to be transmitted over the telephone.The tele-stethoscope 780 is functionally similar to that described inthe 1992 Arthur D. Little report entitled “Telecommunications: Can ItHelp Solve America's Health Care Problems?”. The tele-stethoscope 780permits the MDATA system 100 to greatly expand the spectrum of its soundanalyses to include heart murmurs, the bruits of intracranial aneurysms,breathing sounds like the wheezes of asthma and the rales of congestiveheart failure, or even the bowel sounds of an intestinal obstruction.

There is more information in clinical sounds than can be represented bya two-dimensional pattern matching model. Transforms, e.g., Fourier, areused to shift different aspects of sounds into domains that can bequantified. The sounds are then pattern matched using an n-dimensionalarray. Consider a simple two dimensional array where time is representedon the X coordinate and amplitude is measured on the Y coordinate. Forexample, a cough may be recorded at two times several days apart. Inthis example, the computer 102 superimposes the waveform from one coughupon the other cough. The non-overlapping parts of the pattern bothabove and below represent the difference in the domain being measuredbetween the two sounds. The area under these two curves is integrated toobtain the area. The sum of the areas of the two curves represents thedifference between the two sounds in the domain being measured. Theresultant area is then subjected to one or more sensitivity factorswhich are discussed hereinbelow. Hence, the more sensitive the system,the sooner it makes a match.

In a similar way, a sound pattern may be considered with time on the Xcoordinate and frequency on the Y coordinate. The same methodology isused to quantify the differences between the two curves. Thus, in asimilar way, all aspects of sound can be measured.

In this pattern matching scheme, different weights are given to thedifferent aspects of the sound depending upon which clinical sound ismeasured. In most sounds, the amplitude and frequency are the mostimportant aspects. The weight or the relative importance of an aspect isdifferent for each of various clinical sounds, such as heart murmurs,bruits, wheezes, coughs, stridor and so forth.

When value(s) from any of the procedures 772-780 are procured by thesystem 100, the computer 102 moves to state 782, recites the value andrequests the patient to confirm the value. If the patient indicates thatthe value is correct, as determined at a decision state 784, thecomputer 102 proceeds to state 786 and installs the value into thesymbol table associated with the current patient. If the value is notcorrect, as determined at decision state 784, the computer 102 proceedsto state 790 to determine if the patient would like to try providing thevalue again. If so, the computer 102 loops back to the beginning of thefunction 514. If the patient does not wish to try again, as determinedat state 790, or if state 786 is completed, the computer 102 returns atstate 788 to the evaluation process (FIG. 10 b).

Referring to FIG. 20, the Patient Medical Condition Routine 516 definedin FIG. 10 b will be described. In the course of executing a particularmedical algorithm in the evaluation process 254 (as shown at state 506),the computer 102 may request additional medical condition information ofthe patient. This information reflects the current condition of thepatient, which is in contrast to the information requested by the pastmedical history routine 512 (FIG. 18) for past history information. Thestates 800 through 814 of the routine 516 are essentially the same asstates 740 through 754 of routine 512, except that in routine 512 thecondition label (L) denotes a value for which a past medical historyquestion is to be asked during the evaluation process, while in routine516 the condition label denotes a new value desired by the algorithm.Therefore, states 800-814 are not further described herein.

XVI. Symptom Severity Analysis

Referring now to FIGS. 10 a, 10 b, and 21, the symptom severity analysisfunction 518 defined in FIG. 10 b will be described. A review andfurther description of the re-enter feature, which is associated withsymptom severity analysis, is also provided here.

An important feature of the MDATA system 100 is its ability to follow ormonitor a patient over time. If the MDATA system 100 is in the processof diagnosing a patient's complaint but is not certain what actionshould be taken (states 520-522 of FIG. 10 b), system 100 may ask thepatient to re-enter the system at a designated time, usually within afew hours.

When the patient calls the MDATA system at the designated time, thesystem takes the patient through the initial problem screening questions(state 483 of FIG. 10 a) in order to exclude those problems that requireimmediate medical attention. The system detects that the patient is are-enter case (state 490 of FIG. 10 a), and then determines the re-entrypoint in the evaluation process based upon the patient's previousinteraction with the system (state 492 of FIG. 10 a). For example, ifthe MDATA system established a diagnosis of migraine, that is, if boththe probability of migraine and the probability of confirmation ofmigraine reached threshold values, the patient would not repeat thediagnostic process, which is the usual case.

Occasionally, however, a patient for whom a diagnosis has not beenestablished will be asked to re-enter the system 100. This patient isagain asked the diagnostic screening questions, in addition to theinitial screening questions (state 306 of FIG. 7 a) and problemscreening questions (state 483 of FIG. 10 a). If the MDATA system 100 isnot able to establish a diagnosis for a re-enter patient, he or she isreferred to a physician for further evaluation.

In addition to the re-enter feature, the MDATA system 100 has thecapability to call patients back in order to monitor their progress. Thesame trending methodologies are used regardless of who initiates thecall, i.e., the system or the patient. Using this capability, the MDATAsystem 100 can provide regular or periodic monitoring of elderlypatients in their homes as well as inform patients when a new therapybecomes available.

Many problems for which the MDATA system 100 offers advice have absolutethresholds for the initial quantization of the severity of a symptom.For example, chest pain that is described by a patient as being 10 on a10-scale of severity, would reach the problem-specific initialsymptom-severity threshold and would mandate a consultation with aphysician.

Interestingly, with headache, an initial severity characterized by thepatient as 10 on a 10-scale would not, in itself, necessarily require animmediate consultation with a physician. If, in addition, the headachecame on suddenly and, as was mentioned earlier, was described as theworst headache of the patient's life, the MDATA system 100 wouldconsider this to be suggestive enough of a subarachnoid hemorrhage toadvise an immediate consultation with a physician.

Continuing in the headache example, after a re-enter patient with anestablished diagnosis is asked the initial and problem screeningquestions, the MDATA system 100 again assesses the severity of thepatient's headache. Reassessing the severity of the headache, by havingthe patient re-enter the system, establishes two points of reference.The system 100 is now able to analyze any changes in the level ofseverity as well as calculate the rate of change in severity over time.

The symptom severity analysis function 518 has a Number of Points (N) asan input parameter as indicated at state 830. Number of Points refers tothe points of reference established during the initial consultation fora particular problem and during subsequent re-enter consultation(s).Beginning at a start state 832, the computer determines the value of(N), i.e., the number of reference points, at a decision state 834. Ifit is determined that N=2, the computer 102 moves to state 836 tocompute the slope of a line connecting the two reference points usingstandard mathematical techniques. Proceeding to state 838, a variablenamed Power is set to be one because only two reference points are usedat state 836. The computer 102 returns at state 840, with outputparameters Slope and Power as determined by function 518, to theevaluation process (FIG. 10 b).

Using the returned Slope and Power parameters in the evaluation process254, if the MDATA system 100 determines that the severity of theheadache, for example, is increasing too rapidly, that is, if a slope ofthe line connecting two points on a graph of the severity reaches a setthreshold, system 100 will make an appropriate recommendation.

If the MDATA system 100 finds that the severity of the headache isstaying the same or is getting worse but is doing so at a relativelyslow rate, it may ask the patient to re-enter the system a second time(i.e., for a third consultation), usually within a shorter period oftime. The third consultation gives the MDATA system 100 three points ofreference from which to trend the severity of the headache. Thus, whenthe function 518 is called by the evaluation process, the value of (N)is three, as determined at state 834, and the computer 102 branches tostate 844. At state 844, the computer 102 determines the slope and powerof a line connecting the three reference points. The presently preferredembodiment uses the well-known Runge-Kutta method, which is a numericalapproximation technique for solving differential equations. Otherembodiments may use other well-known, standard curve fitting functionsat state 844.

If the system 100 determines that yet one or more additionalconsultations, i.e., beyond three consultations, are desired, e.g., toestablish a trend with certainty, it will again request the patient tore-enter the system at a later time. In this situation, the three mostrecent reference points are used in the calculation at state 844.

The system 100 then performs a “sequential symptom-severity slopeanalysis” to determine if the symptom is getting worse too rapidly asfollows. The slopes of the lines connecting the first and second point,the second and third point, and then the first and third point arecalculated. If any of these reach a problem-specific threshold, theappropriate recommendation is given.

If the sequential symptom-severity slope analysis does not reveal theneed to seek medical attention, then the MDATA system 100, in additionto calculating the rate of change in the severity of the symptom withrespect to time (the slope analysis), now calculates the rate of changeof the rapidity with which the headache is getting worse. This is thefirst derivative.

Table 4 (FIG. 25) illustrates this relationship. Time maps onto the “X”axis and the symptom's severity maps onto the “Y” axis. Note that a lineconnecting these three points forms a gently sloping straight line. TheMDATA system 100 using this data determines that, although the symptomis getting worse, it is doing so in an arithmetical or linear way. Thatis, although the severity of the symptom is increasing, the symptom'srate of change is not increasing.

In contrast, a line connecting the three points on the graph of Table 5(FIG. 26) forms a sharply upturned curve. The MDATA system 100 using thedata of Table 5 determines that, not only is the symptom rapidly gettingworse, but more significantly, the rate at which the symptom is gettingworse is also increasing. In the MDATA system 100, this analysis istermed an “exponential symptom-severity filter.” All patients whore-enter the system a second time are subjected to this analysis.

It is important to note that the severity of a problem, e.g., aheadache, is not necessarily related to the seriousness of theunderlying cause. The MDATA system 100 is programmed such that when anysymptom gets rapidly worse, medical intervention is frequently advisedas necessary. This concept is valid for many symptoms.

Returning to the symptom severity analysis function 518 (FIG. 21), ifthe function 518 is called with N=0, or N=1, the computer branches tostate 842. At state 842, the Slope and Power parameters are set to zero,and the computer 102 returns these parameters at state 840 to theevaluation process (FIG. 10 b). The values set at state 842 essentiallyflag an error condition that is acted on by the evaluation process 254.

XVII. Treatment Table

Referring to FIG. 22, the Treatment Table process 256 defined in FIG. 7d will be described. The MDATA system 100 is modularized in its approachto diagnosis and treatment. In medicine, diagnosis simply means figuringout what is causing the problem, and treatment refers to what actionshould be taken once the cause of the problem is known.

Diagnosis is composed of history, physical examination, imaging studies,and laboratory tests. Again, history is by far the most important factorin making the diagnosis. In fact, in medical school, students are taughtthat if they don't have a good idea of the diagnosis by the end of thehistory, they are doing something wrong.

The treatment side of medicine is conceptually different from diagnosisin that, while the basic principles of making the diagnosis remain thesame, treatment is continually changing. Treatment is fundamentally a“look-up” table with the diagnosis, age and sex on the left and the mostcurrent treatment on the right as shown in Table 6. Or, treatment can bethought of like the cubbyholes or boxes of a post office. Eachindividual box holds the treatment for a given disease. The informationgiven is age and sex specific. The contents of the box are constantlychanging, but the location of the box does not. For example, what isthought to be the best antibiotic to treat meningitis in a two-year-oldchild could literally change from week to week as more antibiotics aredeveloped and approved or more controlled studies are published.

TABLE 6 Simplified TREATMENT TABLE Example Diagnosis Treatmentmeningitis in a antibiotic of choice two-year old child as of currentdateThe MDATA system 100 maintains a treatment table that can be updatedinstantaneously to provide the most current treatment recommendations.

The treatment table can be directly accessed by patients who alreadyknow their diagnosis. For example, asthma patients can consult thesystem as often as they wish to see what the absolute latest treatmentis for their condition. In fact, links are maintained between thetreatment table and the patient medical history files 268. In this way,when a new treatment is introduced for any of the ICD-9-CM codes listedin the MDATA system 100, patients can be contacted and asked to eithercall the system 100 back at their convenience or have the MDATA system100 fax or mail the information to them. The MDATA system 100 can alsonotify patients' doctors when a new treatment is identified.

The concept of using a table is also helpful with regard to two aspectsof the diagnostic process that often do change: the imaging modality ofchoice (like X-ray, Computerized Tomography (CT), Magnetic ResonanceImaging (MRI)), and the laboratory test(s) of choice. Therefore, theMDATA system 100 also maintains a table for imaging modality of choiceas well as laboratory test(s) of choice in the work-up or diagnosis of aparticular complaint. By modularizing these aspects of the diagnosis, asnew imaging techniques, like Positron Emission Tomography (PET)scanning, and new laboratory tests, like recombinant DNA technology, arediscovered, only the tables have to be altered, not the medicalalgorithms themselves.

The treatment table will be further described in a general way asprocess 256 in FIG. 22. The treatment table process 256 begins at startstate 860 and proceeds to state 862 wherein the computer prompts thecaller to choose a treatment selection method:

-   -   i. Treatment selected from layered menus, or    -   ii. Treatment selected via direct entry of a catalog number.

The first selection method entails the use of the menu-driven treatmentselection process 864 which will be described hereinbelow in conjunctionwith FIG. 23. The second selection method at state 866 uses a treatmenttable catalog message number. This catalog is part of the patientinformation package, a section of which appears in Table 7. Thetreatment table catalog is organized by anatomic area and diagnosis, andwhen applicable, by age and gender. After the patient selects a catalognumber, the computer 102 stores the selection in a memory variable ‘M’.As an alternate selection method, the system 100 allows the patient todirectly enter the ICD-9-CM code for their problem. In this case, thecomputer 102 will look-up the ICD-9-CM code in an internalcross-reference table to identify the catalog number, and set the memoryvariable ‘M’ to this catalog number.

TABLE 7 Portion of Treatment Table Catalog NEUROLOGY Diagnosis MessageEpilepsy 1101 Meningitis 2 years old & younger 1201 over 2 years old1202 Depression Male Under age 50 1301 50 years and older 1303 FemaleUnder age 50 1302 50 years and older 1304

Once the value of the memory variable ‘M’ is established by process 864or state 866, the computer 102 moves to state 868 and plays treatmentmessage ‘M’ to the patient. At the conclusion of treatment messageplayback, the computer 102 moves to a decision state 870.

At state 870 the computer 102 checks for existence of society message‘M’. The society message category contains information aboutorganizations that assist patients with a particular disease. If thesociety message ‘M’ does not exist, the computer 102 moves to a decisionstate 874. Otherwise, the computer 102 will move to state 872 wherein itplays society message ‘M’ to the patient. At the end of the societymessage ‘M’, the computer moves to state 874.

At state 874, the computer 102 checks for the existence of anover-the-counter (OTC) message ‘M’. The OTC message category containsinformation about generally available over-the-counter medications andhome treatment for a particular diagnosis. If the OTC message ‘M’ doesnot exist, the computer moves to state 878. Otherwise, the computer 102moves to state 876 wherein it play OTC message ‘M’ to the patient. Atthe end of the OTC message ‘M’, the computer 102 moves to state 878.

At state 878 the computer 102 plays a terminal menu to the patient whichallows the patient to either select another treatment, or to exit fromthe treatment table process 256. If the patient wishes to hear anothertreatment message, the computer 102 moves back to the treatmentselection method menu state 862. If the patient wishes to exit thetreatment table process 256, the system moves to state 880, wherein thetreatment table process 256 terminates and returns to the top level flow(FIG. 7 d) at state 344.

An example of the treatment, society and OTC messages for epilepsy aregiven in Table 8. Note that since the OTC message is empty, the computer102 would skip over the OTC message playback and proceed directly to theterminal menu.

TABLE 8 Treatment Table Messages for Epilepsy Treatment Message As ofDec. 20, 1993, according to Emergency Medicine: Concepts and ClinicalPractice, Third Edition, by Drs. Rosen, Barkin, et. al., pages 1800 and1801, the initial treatment of generalized tonic-clonic seizures, i.e.,grand mal seizures, is as follows: After efforts to discover and treatacutely correctable causes like hypoglycemia, the followingpharmacologic agents are indicated: 1. Intravenous administration oflorazepam, with a loading dose of 0.1 mg/kg and an infusion rate not toexceed 2 mg/min. Which is usually followed by: 2. Intravenousadministration of phenytoin, with a loading dose of 15 to 18 mg/kg andan infusion rate not to exceed 0.75 mg/kg per minute. If lorazepam isnot effective, and in those individuals allergic to phenytoin: 3.Intravenous administration of phenobarbital, with a loading dose of 8 to20 mg/kg and an infusion rate not to exceed 0.75 mg/kg per minute. Ifthe above is not successful: 4. A neuromuscular blocking agent likepancuronium, with an intravenous dose of 0.03 to 0.1 mg/kg. 5.Intravenous administration of paraldehyde, with a loading dose of 0.1 to0.15 ml/kg, diluted with saline to a 4% to 6% solution and slowlyinfused over 1 hour. Society Message “For further information onepilepsy, contact: Epilepsy Foundation of America 1828 L Street, N.W.,Suite 406 Washington, D.C. 20036 (202) 293-2930 In addition to thenational headquarters, there are 100 local chapters. The San Diegochapter can be contacted at (619) 296-0161.” OTC Message None.

Referring now to FIG. 23, the menu-drive treatment selection process 864defined in FIG. 22 will be described. The menu-driven treatmentselection process 864 begins with start state 890 and proceeds to state892 wherein the computer 102 recites an area menu to the patient andrequests selection of one area. The complete menu is not shown in state892. The areas are arranged by anatomic system. For example, if apatient has epilepsy, the patient can simply select this from theanatomic system menu for the neurological system.

Based on the patient's selection, the computer 102 branches to aselection area menu state, such as neurological area menu state 894,wherein the computer 102 recites a list of diagnoses to the patient andrequests selection of a diagnosis. In some cases the diagnosis isfurther subdivided by gender, age or both gender and age. At state 904,for example, for a diagnosis of meningitis, the computer 102 wouldprompt the patient to select from a secondary menu between a treatmentfor a child two years old or younger and a treatment for somebody overtwo years old. Then, based on the patient's selection, the computer 102sets a memory variable ‘M’ to the value of the selected diagnosismessage number at state 908 or 910. State 906 is another examplesecondary level menu which has four choices based on gender and age.These four choices are associated with four states, 912, 914, 916, 918,wherein the computer 102 sets the memory variable ‘M’ to the value ofthe diagnosis message number that was selected at state 906. After thecatalog number has been stored in memory variable ‘M’, the computer 102moves to return state 923 wherein the menu-driven treatment selectionprocess terminates and returns control to the treatment table process256.

Area2 menu 896 and AreaN menu 898 are indicative of menus similar tomenu 894 but for different anatomic systems. Menu 896 and 898 may havesecondary menus, similar to menus 904 and 906 under menu 894. Then,states 920 and 922 are indicative of the computer 102 setting memoryvariable ‘M’ to the value of the diagnosis message number selected fromthe parent menu 896 or 898, respectively.

XVIII. The MDATA System Paradigm

The MDATA system paradigm is based on several fundamental principles.They are as follows:

-   -   Centralization of medical information    -   Accessibility of medical information    -   Modularity of medical information    -   Modifiability of the system.

As mentioned earlier, one of the purposes of the MDATA system 100 is tobring together highly qualified medical experts, encode their knowledgein a central location, and make this information available to everyone.

Although the issue of accessibility has been discussed several times, itis important to understand its significance. Accessibility in the MDATAsystem 100 refers both to the way in which the medical information canbe retrieved from the system 100 by non-medically trained personnel aswell as to the need for people everywhere to easily and promptly obtainmedical information. By using the already established worldwidetelecommunications network, the MDATA system 100 can provide universaland nearly instantaneous access to high quality, 100%-consistent medicaladvice.

In the MDATA system 100, the concepts of modularity and modifiabilityare inextricably intertwined. Modularity is the key to the MDATAsystem's ability to provide patients with the most current medicalinformation available. The MDATA system's modular design and objectoriented techniques allow the individual components of the system to bemodified or updated without generating a ripple effect on otherinformation in the system 100.

In contrast, the print media suffers from an inability to quickly adaptto changing information. Once a book or journal is published, it cannotbe modified until its next publishing date. The MDATA system 100,however, can be modified within hours of a new discovery in medicine.Easy modifiability is another way in which the MDATA system 100 isqualitatively different from previously published algorithms.

Once the medical algorithms for the MDATA system 100 are written andprogrammed, they can then be continuously updated and refined asadvances in medicine are made. Unfortunately, physicians today aresimply not able to keep up with the explosion of new medical informationand technology. This ability to nearly instantaneously modify the MDATAsystem 100 is a powerful feature of the system.

It is presently possible for a computer to search the world's medicalliterature daily. Any articles pertaining to a particular topic canautomatically be requested and the information used to update thesystem.

In addition, the MDATA system 100 is currently using optical characterrecognition technology to digitize its medical database. Then, usingindexing techniques, the MDATA system 100 is able to search for andretrieve any information desired. For example, the system can search forthe character string “headache” and retrieve any amount of surroundingtext or graphic information. This information is then collected,collated, printed and referred to the physician(s) maintaining theheadache algorithm. This process will become easier as more of theworld's medical literature is digitized.

Global Factors—Sensitivity and Selectivity

Another way in which the MDATA system is modifiable is in its use ofglobal sensitivity/selectivity factors. As with every decision, there isalways a balance to be achieved between risk and benefit, and so withthe MDATA system 100. One of the questions the MDATA system 100 tries toanswer is whether the patient needs to be seen immediately by aphysician. This leads to this discussion about sensitivity andselectivity.

Sensitivity and selectivity are statistical terms that refer to howaccurately a decision can be made. In this case, sensitivity refers tothe number of patients which the MDATA system 100 did not think neededto be seen by a physician but that actually did.

If the program were to be so sensitive that no disease process thateventually required meaningful physician intervention would be treatedat home (no false negatives), then every single complaint wouldnecessitate a visit to the doctor, which is a useless system. On theother hand, too selective a system (no false positives) i.e., nounnecessary visits to the doctor's office, would necessitate that anattempt be made at home treatment for every complaint, which is auseless and dangerous system.

So again, a balance must be reached between these two ends of thespectrum. To achieve this, the sensitivity/selectivity ratio of theentire MDATA system 100 can be changed by setting or tuning a pluralityof sensitivity factors. These sensitivity factors affect the followingfunctions: meta thresholds, reenter horizon threshold, frequency of callback, symptom-severity filters, sequential slope filters, exponentialsymptom-severity filters, and probabilities of diagnoses in thetreatment table. In addition, as in the headache example, the scoring ofthe screening questions already weighted is modulated or modified by thesensitivity factors.

Experience from the regionalization of trauma centers in this countryshows an interesting trend over time with respect to sensitivity andselectivity. It has been shown that the inverse relationship betweensensitivity and selectivity, when plotted over time, yields a sinusoidalwave form in which the amplitude of the wave form gradually decreaseswith time as the system is “fined tuned” as shown in Table 9 (FIG. 27).The MDATA system's sensitivity factors are designed to do just that,i.e., fine tune the system over time to find the right balance betweensensitivity and selectivity.

In addition to the use of global factors, the MDATA system 100 maintainswhat are termed “emergency filter response sets.” When a patient replies“yes” to any of the problem screening questions, the recommendation ormessage that follows is called an emergency filter response or “EFR.”The EFR sets are modularized so that the system can customize themessage that the patient hears. This allows the system 100 to match theEFR sets to the desired level of sensitivity or selectivity as well asprovide information specific to an HMO or Managed Care Plan.

System Sensitivity Factors

There are ten sensitivity factors that affect threshold determination inthe MDATA system 100:

-   -   S1=system-wide (usually established by the system administrator        and affects the entire MDATA system)    -   S2=the anatomic system of the body involved (e.g., nervous        system in headache example)    -   S3=cause (e.g., infection causes meningitis)    -   S4=problem specific (established by the algorithm author at the        beginning of an algorithm)    -   S5=question specific (within a particular algorithm)    -   S6=organizational specific (e.g., HMO, hospital)    -   S7=patient specific    -   S8=a reserved sensitivity factor for later use    -   S9=a reserved sensitivity factor for later use    -   S10=a reserved sensitivity factor for later use

Initially, the sensitivity factors have a value of 1.0. The sensitivityfactor's value is usually inversely proportional to sensitivity; i.e.,if the value is decreased, sensitivity increases.

The sensitivity factors are applied to the threshold constant value inthe relational expression component of an IF-Then or If-Then-Elsestatement of a medical algorithm. For example, let's assume that thesystem 100 is in the meningitis algorithm and that a temperature greaterthan 102 degrees will trigger a recommendation to go to a hospital. Anexample of a threshold calculation without sensitivity factors follows:If temp>102 then X else Ywhere X denotes the recommendation to go to the hospital and Y denotes adifferent branch point. Following is the same example, but includingsensitivity factors:If temp>(102*S1*S2*S3*S4*S5*S6*S7*S8*S9*S10) then X else Y

The use of the sensitivity factors permits anticipation of change.Tuning the initial product of the sensitivity factors from “1.00” to“0.95” would decrease the temperature at which the system recommends atrip to the hospital. Each threshold calculation or other use of thesensitivity factors may use any number of (e.g., two factors) and anycombination of the factors. Additionally, any combination of factors maybe modified from the initial 1.0 value in any particular thresholdcalculation.

Age criteria is also modified by use of the sensitivity factors. Forexample: If Age >45*S1*S4 then X else Y.

Examples of areas the system 100 could be tuned follow:

-   -   Anatomic system (e.g., cardiovascular)—the system is missing too        many heart attacks;    -   Cause (infection)—the system is missing too many injuries        (trauma);    -   Problem specific (e.g., headache)—the headache algorithm is        missing too many cases of meningitis or subarachnoid bleeds;    -   Question specific (each question in an algorithm can be        modified)—this would change the “weight” of a question in a        series of weighted questions like the migraine screening        questions;    -   Patient specific—one patient might want to be VERY careful while        another might say, “in general I don't go to the doctor until        I'm really sure something is wrong with me”;    -   Organizational (e.g., Kaiser patients)—Kaiser hospital        management may say that the system is missing too many cases of        meningitis and may request to be more careful with their        patients (send them in with a lower temperature).

The sensitivity factors affect the following system 100 functions:

-   (a) Re-enter Feature—the sensitivity factors affect the re-enter    horizon, i.e., the amount of time after which the system 100    considers a repetition of the same complaint to be a new problem. If    sensitivity increases, the re-enter horizon becomes sooner.-   (b) Meta Function—the sensitivity factors affect the matching and    time density ratio thresholds. By reducing the values of the    system-wide and problem sensitivity factors, e.g., from 1.0 to 0.9,    the matching threshold and the time density ratio are decreased:    -   Example 1 Without Sensitivity factors:        Meta(“NHDA”,“****”,“**********”, 06/01/93, 12/31/93)    -   If MC>=3 then 100 else 101    -   Example 1 With Sensitivity factors:        Meta(“NHDA”,“****”,“**********”, 06/01/93, 12/31/93)    -   If MC>=(3*S1*S4) then 100 else 101    -   Example 2 Without Sensitivity factors:        Meta(“****”,“****”,“I*********”, 06/01/93, 12/31/93)    -   If TDR>=2.0 then 200 else 201    -   Example 2 With Sensitivity factors:        Meta(“****”,“****”,“I*********”, 06/01/93, 12/31/93)    -   If TDR >=(2.0*S1*S4) then 200 else 201    -   Thus, there is no necessity to change the algorithms themselves.        In other words, the factors can be modified rather than changing        the algorithms.-   (c) Problem Questions—To take the headache example previously used,    the sum of the scores of the screening and confirmation questions    (and sometimes the questions themselves) is multiplied by the    sensitivity factors. The questions are also weighted, of course,    depending upon how important each question is to the diagnosis. The    sum of the weighted scores is compared against the threshold value    that will result in either making the diagnosis of say migraine (in    response to the migraine screening questions) or confirming the    diagnosis of migraine in response to the migraine confirmation    questions.    -   Thus, if we wanted to increase the sensitivity of diagnosing        subarachnoid hemorrhage, we would not have to write another        algorithm, but rather, simply multiply the screening and        confirmation scores by the sensitivity factors.    -   For example, if the threshold for the MDATA system 100 to make a        diagnosis of subarachnoid hemorrhage based on the sum of the        weighted subarachnoid screening questions threshold is set at,        say 75%, then that percentage of the sensitivity variable would        make this diagnosis with a smaller score and, thus, pick up more        cases. Thus, individual diagnoses within an algorithm can be        “tuned” independently, and in some cases, this even applies to        the individual questions themselves.-   (d) Symptom Severity and Symptom Severity Trend Analysis—the    sensitivity factors alter the absolute value, the first, second and    third slope thresholds. With increased sensitivity, a more gently    sloping line triggers an earlier medical evaluation. In the    algorithm, when the system 100 makes use of any quantitatable    parameter to make a decision, all of these are joined, influenced or    multiplied by the sensitivity factors. As a very simple example, if    the MDATA system 100 would normally make a recommendation, partly    based on the age of the patient (e.g., if you are male and you are    over 50 and . . . ), the decision can be triggered if the patient is    49 or 48 and so on.-   (e) Home Diagnostic and Treatment Kit—if the patient has a MDATA    system treatment kit or a blood pressure cuff, the level at which a    fever or blood pressure effects a decision can be changed.-   (f) Mental Status Examination—the mental status examination can be    modified at a system, or problem (algorithm) level.-   (g) Clinical Sound Library—the pattern matching process (as in the    clinical sound library) is quantifiable by modifying the sensitivity    factors.

XIX. VIDEO IMAGING OF THE PATIENT

There are four main types of video imaging: static black and white,static color, video black and white and video color. Each of these maintypes is now discussed.

Images as basic as static black and white images can provide usefulinformation to the system 100. Static black and white imaging is usedwith neural net pattern matching. This process permits analyzing forexample, facial features to aid in the detection of certain diseases,such as the characteristic facies of Cushing's syndrome or theexopthalmos of Graves disease.

Color static imaging allows color frequency analysis to detect diseasesthat are not as readily detected with static black and white imaging,such as cyanosis of respiratory failure or the scleral icterus ofhepatitis. Color thus provides an incremental benefit in the level ofdisease detection.

Real time black and white video imaging allows for the evaluation ofphysical signs such as pupillary responses, extra ocular musclefunction, lid lag, and nystagmus. Cranial nerve function can be remotelyevaluated, along with, for example, the distinction between central andperipheral VII nerve function.

Color video imaging, especially using fiber optics, adds much morecapability in the evaluation of a patient's condition. For example,color video imaging is very useful in evaluating capillary refill ormonitoring the response of a patient with cyanosis to supplementaloxygen. Another embodiment of the system 100 may employ inexpensivelaser sources to perform real time holographic imaging.

XX. Benefits of the MDATA System

It is rare when the humanitarian and entrepreneurial interests of aventure overlap. The confluence of purpose that exists in the MDATAsystem is striking. It is a “win-win” proposition from everyperspective.

Not only will the MDATA system 100 substantially reduce the overwhelmingcosts of our current health care system, but for the first time inhistory, every person can have access to high quality, 100%-consistentand affordable medical advice and information. No matter from whatperspective one views the MDATA system 100, its benefits aresubstantial.

The health care consumer obviously gains the most. Now, whenever he orshe has a medical problem, or any member of their family, an immediateconsultation can be obtained. The knowledge that the best health careinformation and medical advice is only a telephone call away can assuagethe anxiety of everyone from new mothers to elderly patients confined totheir homes.

By endorsing the MDATA system 100, federal, state and local governmentscould discharge their obligation to provide a universal and affordablelevel of health care for all of their citizens. In addition, the MDATAsystem 100 helps care for patients who cannot pay, thus relievingprimary care physicians of the necessity to provide care withoutreimbursement. For the first time, Health Maintenance Organizations andManaged Care Plans will be able to effectively screen patients bytelephone in order to ensure that patients are best matched with theservices they need.

Specialists can use their talents, not on the repetition of familiarrituals, but will be free to concentrate on those more challengingproblems that cannot easily be resolved by the MDATA system 100. Theywill also benefit from an increased number of patient referrals as wellas having a well-constructed patient history when a consultation issought.

Physicians themselves can access the MDATA system 100 in order to stayinformed about new information and technological advances in the medicalfield. This is particularly true with the treatment, imaging, andlaboratory test databases.

Medical information is a continually renewable resource because it isnot consumed in its dissemination. The opportunity exists, through theMDATA system 100, for the United States to provide much needed medicalinformation to the world and, at the same time, bring capital into thiscountry. In the process, this country could maintain its leadership ininnovation, technology, and software development.

The United States and the world are facing a health care crisis somonumental that it is difficult to comprehend. There are diseases thatthreaten our very survival as a species. All of us know the apprehensionand bewilderment we feel when an illness strikes. When this occurs, weneed answers to specific medical questions, answers that are absolutelyup-to-date, instantly available, and affordable.

The key is information: information about prevention, early detection ofdisease, and about its most efficient treatment. The MDATA system 100can provide this information through the simple use of the telephone, tonearly every inhabitant of the planet. In addition, the MDATA system 100converts and explains complicated medical terminology and concepts intolanguage easily understood by everyone.

People do not have to be ill to consult the MDATA system 100, justcurious. Patients do not have to schedule appointments, they can simplypick up the telephone. Although many patients will later be seen by aphysician, the MDATA system 100 can provide immediate help for everyone.The MDATA system 100 at once establishes egalitarian access to healthcare information. Although many patients in this country receivestate-of-the art medical care, there is a large segment of thepopulation that is deprived of one the most basic health care andmedical information. The MDATA system 100 begins to close this enormousgap.

The MDATA system 100 begins to effect a restructuring of the health caredelivery system in which both health care consumers and providersparticipate in the improvement of the system itself. The MDATA system100 and its patients will be in partnership to provide the most current,economical, and concise treatment available. The upside potential isunlimited. Whether one believes health care is a right or a privilege,there can be no doubt that it is fundamentally necessary. Whether onebelieves we have a civic responsibility or a moral obligation to carefor one another, it must be done. The fundamental simplicity of thestructure of the MDATA system 100 belies its power as a highly usefultool in the delivery of health care.

XXI. Optional System Configuration

A second embodiment of the MDATA system entails a major shift of how thequestions and responses are delivered to the patient. Rather than theuse of a telephone, the voice processing and voice response technology,the system software is published via media such as floppy disks, CD ROM,or PCMCIA cards for use on a patient's personal computer. This secondembodiment is referred to as the screen version or the (Stand-Alone)SA-MDATA system. The computer could be, for example, a desktop computer,a laptop or notebook computer, or a handheld, pen-driven computer. Thesystem questions are displayed on a display screen that is part of thecomputer or is connected to the computer. The patient uses a keyboard ora pointing/writing device connected to the computer to respond to thequestions. The patient files are maintained and updated within thecomputer or on removable storage devices. The diagnosis, advice, andtreatments can be displayed on the screen and also printed in hardcopyform on a printer (if available). New versions of the SA-MDATA systemare either mailed to subscribers are available via modem. These newversions may include updates of the treatment table for new treatments.Another embodiment of the SA-MDATA system may include using specializedreceiver devices that receive encoded FM signals on a demand basis whenan event (a new treatment) triggers the device, such as described inU.S. Pat. No. 5,030,948.

A unique and separate authoring language (called File Output or FO) wasused to develop the medical algorithms used in the screen versionembodiment of the system 100. Through the use of FO, the contents oftext files are presented online to users, and then the users respond toquestions and directions issued by the text files.

FO is designed as a typical, generalized authoring language, in whichcommands are embedded into text files (herein called FO files) toperform specific screen and keyboard functions. FO files are in effectprograms written in the FO “language” that communicate (via FO) with theuser online.

FO adds no text of its own. In fact, FO does not need to know what textfile content it is executing. The programmer or author of a FO file isin complete control of the text content and the sequence in which it ispresented. Using the various commands described in the AuthoringLanguage Syntax, the author can display text, format the screen, ask theuser questions, input responses from the user, select different textfiles for execution, and generally control and direct the entiresession.

This version of FO is intended as a development version that gives theuser much freedom at the keyboard. The user can interrupt a presentationand edit the FO file being presented. The assumption here is that theuser is in fact the author or an alpha tester charged with verifying andcorrecting file content.

A FO file is any standard sequential ASCII text file withvariable-length lines terminating with a Carriage Return (ASCII 13). Anyline with a period in column one is treated as a command. A line withouta leading period is treated as a print command.

The FO program processes a FO file by reading it one line at a time intomemory. If the line is a text line, it is printed and the next line isloaded. If the line is a command line, the command is executed. If thecommand involves a wait on the user (such as a .M command), FO continuesloading the FO file behind the scenes until it has been completelyloaded. In this manner, FO executes the FO file as it is loading it.Once loaded, the FO file remains entirely in memory.

The system software for the screen version embodiment is written inBorland Turbo Pascal version 3.0. A second version of the systemsoftware for the screen version embodiment of the system 100 is writtenin Microsoft G.W. Basic and is run in interpretive mode.

In yet other embodiments, other databases/files or algorithms can beused. The general system, method and procedures would remain the same.For example, a specialty field such as sports medicine could be added tothe system.

The MDATA system 100 described herein finds application in manyenvironments, and is readily adaptable for use therein. For example, thesystem finds use in any application that is step-oriented and can bealgorithmically described. For example, the system could give cardiagnostic services over the phone to a caller. Then, when the car isbrought to a service facility for repairs (treatment), the caller willbe informed and have a good idea of what the problem is and probablerepairs will be. Accordingly, the claims are to be interpreted toencompass these and other applications of the invention within theirscope and are not to be limited to the embodiments described herein.

XXII. Summary of Advantages of the Present Invention

One of the main problems of the health care crisis is the limited accessto health care information when it is needed. The MDATA system providesup-to-date medical information and advice that is instantly availabletwenty-four hours a day. The advice that is given is 100% consistent.

The quality of the advice is much better if a physician can stop,research, and anticipate all possible causes of a problem and thensystematically go about dealing with all of these possible causes. Inmedical practice, a physician just does this from memory.

No humans are necessary to actually give the medical advice. The MDATAsystem is automated which helps to bring down the cost of health care.

An exact record of the questions asked and the answers given is storedin the patient's database. The MDATA system time-and-date stamps theresponses to the questions (as transaction records) so that an exactreconstruction of the patient's interview(s) can be generated for use bya physician or other health care professional. The system also keeps arecord of what version of an algorithm has been consulted as well as thesensitivity factor set for that consultation. At the conclusion of theinteraction, the MDATA system can tell the patient how long theconsultation has taken and what charges have been incurred, if any.

When possible, the MDATA system 100 takes into account the past medicalhistory of the patient, especially those pieces of information learnedfrom past consultations with the MDATA system 100, before advice isgiven. In addition, the advice given is different depending upon the ageand sex of the patient. The “meta” functions provide another advantageby allowing the MDATA system 100 to evaluate a problem in the context ofthe patient's prior consultations with the system.

While the above detailed description has shown, described and pointedout the fundamental novel features of the invention as applied tovarious embodiments, it will be understood that various omissions andsubstitutions and changes in the form and details of the deviceillustrated may be made by those skilled in the art, without departingfrom the spirit of the invention.

1. A computerized medical self-diagnostic system comprising: a memory ina computing device configured to store a data structure, wherein data inthe data structure is referenced by specification of a plurality ofattributes representative of a medical condition of a patient; aninterface configured to receive at least one attribute via a directinteractive dialogue between a patient and the computing device, whereinthe data structure remains unchanged by the receiving at least oneattribute via direct interactive dialogue, wherein a first storedattribute corresponds to a cause of disease and a second storedattribute corresponds to an anatomic system stored in said datastructure; and a processor in a computing device, in data communicationwith the memory, configured to make a self-diagnosis of a new andchanging health condition of and to the patient without input from ahealth care provider based on the data in the data structure and thespecification of at least one attribute determined via said directinteractive dialogue.
 2. The system of claim 1, wherein the diagnosis isa single disease.
 3. The system of claim 1, wherein the diagnosis is adifferential diagnosis list.
 4. The system of claim 1, wherein eachcause of disease is subdivided into subcauses of disease.
 5. The systemof claim 1, wherein each anatomic system is subdivided into anatomicsubsystems.
 6. The system of claim 1, wherein a third attribute is time.7. The system of claim 1, wherein the data structure is atwo-dimensional array.
 8. The system of claim 1, wherein the datastructure is a three-dimensional cube.
 9. The system of claim 1, whereinthe processor may contact a health care provider if the specification ofthe at least one attribute determined by said interactive dialoguereaches a predetermined threshold.
 10. A computerized method of medicalself-diagnosis, the method comprising: receiving an input from a patientvia direct interactive dialogue between the patient and a computingdevice; associating said input with at least one stored attribute;ascertaining a plurality of stored attributes of a medical condition ofthe patient, wherein a first stored attribute is associated with a causeof disease and a second stored attribute is associated with an anatomicsystem; referencing a data structure based on a specification of atleast one of the attributes, wherein the data structure remainsunchanged by the receiving an input; and self-diagnosing of a new andchanging health condition of and to the patient without input from ahealth care provider based on at least one of said specification of theplurality of stored attributes and the data in the data structure andthe specification of at least one attribute determined via said directinteractive dialogue.
 11. A computerized method of medicalself-diagnosis, the method comprising: receiving an input from a patientvia direct interactive dialogue between the patient and a computingdevice; associating said input with at least one stored attribute;associating a data structure, stored in a memory on the computingdevice, with the patient, wherein data in the data structure isreferenced by specification of a plurality of stored attributesrepresentative of a medical condition of the patient, wherein the datastructure remains unchanged by the receiving an input, wherein a firststored attribute corresponds to a cause of disease and a second storedattribute corresponds to an anatomic system; ascertaining a plurality ofattributes of a first medical condition of the patient; ascertaining aplurality of attributes of a second medical condition of the patient;referencing the data structure based on a specification of at least oneof the plurality of attributes; and outputting a self-diagnosis of a newand changing health condition of and to the patient without input from ahealth care provider based on the specification of at least one of saidplurality of stored attributes and the data in the data structure andthe specification of at least one attribute determined via said directinteractive dialogue.
 12. The method of claim 11, wherein the diagnosiscomprises a third medical condition.
 13. The method of claim 11, whereina third attribute corresponds to time.
 14. The method of claim 11,wherein the first medical condition and second medical condition are thesame medical condition.
 15. The method of claim 11, wherein the firstmedical condition and second medical condition are associated withdifferent times.
 16. The method of claim 11, wherein each cause ofdisease is subdivided into subcauses of disease.
 17. The method of claim11, wherein each anatomic system is subdivided into anatomic subsystems.18. The method of claim 11, wherein associating said input with at leastone stored attribute further comprises a comparison of a storedattribute of the patient with the input, wherein the system may contacta health care provider if the comparison reaches a predeterminedthreshold.
 19. A computer usable medium having computer readable programcode embodied therein for performing a computerized process used inmedical self-diagnosis, the computer readable code comprisinginstructions for: receiving an input from a patient via directinteractive dialogue between the patient and the computer usable medium;associating said input with at least one stored attribute; associating adata structure, stored in a memory on a computer with the patient,wherein data in the data structure is referenced by specification of aplurality of stored attributes representative of a medical condition ofthe patient, wherein the data structure remains unchanged by thereceiving an input, wherein a first stored attribute corresponds to acause of disease and a second stored attribute corresponds to ananatomic system; ascertaining a plurality of attributes of a firstmedical condition of the patient; ascertaining a plurality of attributesof a second medical condition of the patient; referencing the datastructure based on a specification of at least one of the plurality ofattributes; and outputting a self-diagnosis of a new and changing healthcondition of and to the patient without input from a health careprovider based on at least one of said plurality of attributes and thedata in the data structure and the specification of at least oneattribute determined via said direct interactive dialogue.
 20. Acomputerized medical self-diagnostic system comprising: means forreceiving an input from a patient via direct interactive dialoguebetween the patient and a computing device; means for associating saidinput with at least one stored attribute; means for associating a datastructure, stored in a memory on a computer, with the attributeassociated with the input from the patient, wherein data in the datastructure is referenced by specification of a plurality of storedattributes representative of a medical condition of the patient, whereinthe data structure remains unchanged by the receiving an input, whereina first stored attribute corresponds to a cause of disease and a secondstored attribute corresponds to an anatomic system; means forascertaining a plurality of attributes of a first medical condition ofthe patient; means for ascertaining a plurality of attributes of asecond medical condition of the patient; means for referencing thestored data structure based on a specification of at least one of theplurality of attributes; and means for outputting a self-diagnosis of anew and changing health condition of and to the patient without inputfrom a health care provider based on at least one of said plurality ofattributes and the data in the data structure and the specification ofat least one attribute determined via said direct interactive dialogue.21. The system of claim 20, wherein the means for outputting comprises amodem.
 22. The system of claim 20, wherein the means for outputtingcomprises a monitor, a telephone, or a speaker.
 23. The method of claim20, wherein the first medical condition and second medical condition arethe same medical condition.